{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 配置参数\n",
    "本程序基于torchvision内置的语义分割模型来实现对黑色指针区域的提取，它依赖指针检测器得到的指针bbox，是在单个指针包围盒内图像中进行分割\n",
    "\n",
    "ref:\n",
    "\n",
    "[1]https://learnopencv.com/pytorch-for-beginners-semantic-segmentation-using-torchvision/ 这里只有如何inference\n",
    "\n",
    "[2]https://towardsdatascience.com/transfer-learning-for-segmentation-using-deeplabv3-in-pytorch-f770863d6a42 这里有如何迁移学习\n",
    "\n",
    "[3]https://towardsdatascience.com/train-neural-net-for-semantic-segmentation-with-pytorch-in-50-lines-of-code-830c71a6544f 这个教程带训练，最简单\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "#%% config the app \n",
    "\n",
    "exp_no = '2022-11-1-1'#实验编号-仪表指针的检测和分割，从头训练10 epoches.用30张8p图像训练，用同样 的数据做测试\n",
    "\n",
    "mode = 'train'\n",
    "# mode = 'test'\n",
    "\n",
    "# folder = \"D:\\\\DATA\\\\multi-pointer-meter\\\\8p_pointer_seg\"\n",
    "folder = \"D:\\\\Downloads\\\\DATA\\\\水表\\\\8p_pointer_seg_data\"\n",
    "# folder =\"C:\\\\Users\\\\lei\\\\Desktop\\\\8p\"\n",
    "\n",
    "Learning_Rate=1e-5\n",
    "width=height=512 # image width and height\n",
    "batchSize=3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 载入库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%% import libs\n",
    "import os\n",
    "import numpy as np\n",
    "import cv2\n",
    "import torchvision.models.segmentation\n",
    "import torch\n",
    "import torchvision.transforms as tf\n",
    "from PIL import Image\n",
    "import skimage\n",
    "\n",
    "import PySimpleGUI as sg\n",
    "from _pointer_meter_helpers import rotate_im_accord_exiftag, load_anno, load_valid_imfile_names\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 定义数据载入类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %% 定义dataset类\n",
    "class BlackPointerSegDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, root, transforms, down_scale_factor=8):\n",
    "        self.root = root # root folder path\n",
    "        self.transforms = transforms # data transformations\n",
    "        self.down_scale_factor = down_scale_factor # 图像缩小为原图的比例\n",
    "                            \n",
    "        self.imgs = load_valid_imfile_names(root)\n",
    "        self.black_pointers = self.get_black_pointer_info()\n",
    "\n",
    "        self.transformImg=tf.Compose([tf.Resize((height,width)), tf.ToTensor(),tf.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))])\n",
    "        self.transformAnn=tf.Compose([tf.Resize((height,width)), tf.ToTensor()])\n",
    "    \n",
    "    def get_black_pointer_info(self):\n",
    "        n_img = len(self.imgs)\n",
    "        black_pointer_lst = []\n",
    "\n",
    "        for ii in range(n_img):\n",
    "            #载入标注信息\n",
    "            anno = load_anno(self.root, self.imgs[ii], self.down_scale_factor)\n",
    "            #获得包围盒信息\n",
    "            boxes = []\n",
    "            for b in anno['bbox']: \n",
    "                boxes.append(b)\n",
    "            \n",
    "            polys = []\n",
    "            for p in anno['ppoly']:\n",
    "                polys.append(p)\n",
    "            \n",
    "            assert(len(boxes)==len(polys))\n",
    "\n",
    "            n_pointer = len(boxes)\n",
    "            \n",
    "            for i in range(n_pointer):\n",
    "                if polys[i] is not None:\n",
    "                    black_pointer={'idx_img':ii, 'bbox':boxes[i], 'poly':polys[i]}\n",
    "                    black_pointer_lst.append(black_pointer)\n",
    "        \n",
    "        return black_pointer_lst\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.black_pointers)\n",
    "    \n",
    "    def __getitem__(self, idx):        \n",
    "        id_img = self.black_pointers[idx]['idx_img']\n",
    "        #载入指针所在的图像\n",
    "        fpath = os.path.join(self.root, self.imgs[id_img])\n",
    "        img = Image.open(fpath)\n",
    "        img = rotate_im_accord_exiftag(img)\n",
    "        img = img.convert('RGB')\n",
    "        #缩小图像\n",
    "        im_sz=(img.size[0]//self.down_scale_factor, img.size[1]//self.down_scale_factor)\n",
    "        img = img.resize(im_sz)\n",
    "\n",
    "        #扣出指针包围盒所覆盖的图像\n",
    "        bbox = self.black_pointers[idx]['bbox']\n",
    "        img_pt = img.crop((bbox[0],bbox[1], bbox[0]+bbox[2], bbox[1]+bbox[3]))\n",
    "        \n",
    "        #获得指针的掩膜图像\n",
    "        poly = self.black_pointers[idx]['poly']\n",
    "        poly[:,0] -= bbox[0]\n",
    "        poly[:,1] -= bbox[1]\n",
    "        mask = skimage.draw.polygon2mask(img_pt.size, poly).astype(np.float32).T\n",
    "        mask = Image.fromarray(mask)          \n",
    "        \n",
    "        #进行变化\n",
    "        Img = self.transformImg(img_pt)\n",
    "        Anno = self.transformAnn(mask)        \n",
    "        \n",
    "        return Img, Anno\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = BlackPointerSegDataset(folder,None)\n",
    "data_loader = torch.utils.data.DataLoader(\n",
    "   dataset, batch_size=batchSize, shuffle=True, num_workers=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "119\n",
      "torch.Size([3, 512, 512]) torch.Size([1, 512, 512])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1d7ec0f0250>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQYAAAD8CAYAAACVSwr3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9T8x133Ue9Kx97vv7gdpB67SJXNsiGXhAEolWikwlJoiA6gGSOwlyB8iDSJ4EARIDEibAwFJHHWZgiQoPgGAJpFgoUoksKoTUNqlQATkh1CKQWrFigUC0tr/vvWfvxWCtZ+219zn3z/vv+93v67uk+773nnvuOfucs/ezn/V3i6riVV7lVV4lS/mkG/Aqr/IqtyevwPAqr/IqG3kFhld5lVfZyCswvMqrvMpGXoHhVV7lVTbyCgyv8iqvspEXAwYR+aKI/IGIfFdEfvWlzvMqr/Iqzy/yEnEMIrIA+N8A/GsAvgfgdwH8NVX9vWc/2au8yqs8u7wUY/gCgO+q6v+uqvcAfgPAl17oXK/yKq/yzHJ4oeN+BsA/Sp+/B+BfPLXzT3yq6Oc+15uiAFQVCqBB0n+BERwZ9s2iEDQImkq8V/VtKGhq31X1z437AKq2rzbxRgBQey/xub/fbJsatbcN0P3tmq6KLG6PzOn8heTbMb23exfb/L9K/50W/+zvIf45bYd44/y9CCB+ESKKfjj/DvwuvY/vrP2an2Fcbt4m6dbYMxhvW77QfkvibDp9pTKci8+17yD5AOdF0o5ybsdnkLkPtm3/k9b/i6rt0xSYtIF//KPv/1+q+uevOe1LAcPe7RpaKSJfBfBVAPjsZxb8t7/15+K7BuCNKo4KvNEFRy14owfcY8FRF1TtROeIZTjJUQ940+7wRu/i/9tm73/UPsIP14/xw/oRfrh+hB+tH+Gf3H+Mt3XB2+MBb48HHO8PWO8X6LEA9wXlvqDcC2QFylFQ7oHlCP8MlKPa/4r+cJr6//E94J+rP8C0P5qiVN+2Nu8ECqltvLGGmvlGWr9Zig9cgRaxgV3svS7+v8DeL/ZdvRPUjwX1Y6B+5P8/BtpHQP1Y0e6A9pFC7xpwUMjHFeWgWA4Vd3cVpTQcSsNSFIelYhHF3VJtm9h3RRQHqel9Q5GGtS3+rA2oAWBt9lxXXbC24gBesLYCVbH/ftm1jWSXn5sCrfUJoDUD+1oLtAlaK9AGtLUAVezVBFITMOTb2xJYFQfIAqio8W3+zzId40miArkXlLcF5Qgsb8X62z2wvAWWe7X/bxWHt4rlTfPXinJfh0P99u/+R//ntad9KVXiewA+lz5/FsAf5x1U9euq+guq+gs/8ROvzpFXeZVbkpcakb8L4PMi8jMi8hGALwP41gud61Ve5VWeWV5ElVDVVUT+LQB/C8AC4G+q6nde4lyv8iqv8vzyUjYGqOpvAfitlzr+q7zKq7yc/FOt3LewVMvw+qdKzhjJRPka3Bn29rWMxwctL8YY3qVkL0XVgntdcK8HHPWAo5on4207YG0FRy04tgX39YC39YC3dcH9uuBYF6xrQasCdYu1NDEXSXgYJtcQX8k9Kbc2Yty7AYFZuJtCi4zeksbP0q+pAijmfVERoAh0FTQRQIq5KQUwu3eDSAFKQ3HvQCmKtRUcSnM3saKIOhjf0Hwk6bXz6FTsuQYmftLzRmoHXc3hbn7Gtr23wNBQUCE46iGAgZ/f6Ed46+5JuinftgN+XO/ww/Vj/Gi9w5t6wJvVXJT36wHH44K6LmjHBTgWSBXIUcxF6a5KWW3QlNUGjjTd+JIH0Jg62gY0XgpDVG2ww11prfd6haCsBg6lAFrtesy9CXdlAqUImiiKj5gmApXiMSXACmNabbGXqrlFAaCIYtWCA5q5DEXCPXkoDVBgTWDeUo9ueho0CCwKi594CruT4r9n/ENxV3GOb/B7GY+NcR50WzK+412IaHdNlxRzUgy5eiyK+Hdir0dOVO8tMFSYj7uqAUTTgoqCN+3uIij8aP0Ib9Y7vNmAQgFWgfhrBoXCQZTZQ2YROgcvacQvUOJzBKh4SM5zgIQfT4rAwsM8MAbaA3qaBReJKmQxgCsV0FUgi6IcpQc2QdDg4GDufig6OMCDwVQFOHiwkxSsvJjCHQ0QGFuQB1Nsg8UvnBIR3fCMBjwMHFw3kiLQlrfZtUIssG4Tx0BNqsxIj3fLIAKMenBaAHoRi61w6UzicQ18L4GheRcZWQPZwh3eeJBTBoUfV2MJAQrrAce6BCjU+wIQGI4EBwcFHzyy9oEkVQdmwIAlDngZOtf0feudT9SOdS17uPZhiwJabfArTFWwL9I+q7OG1TuX/y8D2zdwEEicNsBBFZp6EKMhl9IABiA5OBTRUCmAERBmWafgJYJKnfa3Yz4MHAaNIQ2y2DgHVBaFqA+6DAT5ARMwGKXI/aZJ4UESdp2xXRzwmTmQPUAEuqg/QNj/dmXHmuS9BAYAwRZoR7D3tCfc4W07DKDwo/UjvKl3eJtUiOPxMICCHEuoD9ISU1gxMIYAhdojFztrQMzcg1DdOCfPFi3HwGCbGQUKXRwcpI8CKR6F6ddlNNSZxjA2/TdF0AlPgQ3LYnYGUZRiikdtbm9QgYQqUQIkDqWeVB9OgQWl2ymeJlKMRZnJIw3suN4uMRPPp33H9gZToRAsIYetB0DgeWwN7y0wAFQnJEAhhz+/bQfc++tNvcN9O+DH6x3uPfz5fl2wHl19ICjcS7clVA959tBnDiCpfTAFO5hAQTJAfBLiY5uqjEo3JnabAywEOwABZnuQDgKDiIFGwQ44iKCVgloVpShqExTxGT4ZI6lGGFsoaA8cWWQkz2ZlCxaQZnkFxgcn4/7c9K5BwU0GNDLKrEoEi/AvFzFD0CPlJoAhdFaX6jNeTXe/SDPLmIvR0s4Y3uid/XdQ+HH7KKkQd/jxeoe36wFvjlmFKJYTscoACqE+OFMgW8igIM23Nd2AQmYLZw2QqkH1RHX7vV34+PkENcy/U8kGNFMDaEST5rOl30MpNEYCNG71hm+nyKY20w7g4PaHJopa1EDCfyrOHFYtEKVRErugMLIIs10Y9HSjZlMZjJBFFFXlpDGS9jfh9Yh6zpSBgYQunjj7oG9knXBz0zfnG1wYp8Dj1IRxCfACFNRtCgQF6SA3y9x/rpSbAAaoBhgA5gKrOzdvQcMRSxge73VBRQeHo6sP9n5xtcEA4VgX3NfulqyrJ0odxRKlEigEQ3A1wuwLaolSAQhdhdgFBbZ/rxMkVpGZRWTGpT46ZxFKwQgO1CPztsWMiwEQqhwTcIywY4lawlYpZlTlIImMzR1wUB5AjHiIdUwtgrYUtNZsoDbjvCIaKoUysUlG+0KRbneI03hGbKFFQ/r3IjqAA20aNX2P9N4M9DbaHSb93qIPqOIbhPaenQGVN+0NxPis3dawJ7t94sTO6REEU0iMYWYN2W2pvKZHyG0AwxOkqXRQ0G5XeNsW3PuL8Qq0K6zrpEI4GAw2haxKrJrsCYjsyDG2gTM+BsPiJyHiTESSSkBPxNhfG4oUaFXbbeib/LDPHGi4awJzU1ZBq6ZStGZ2B5FqmY0qKD7QheAwsYNrZA9AniIiDhRikMFYD6phu5ceP36WJjxcko1BNsbHDA7ypCa+t8BAb8RRD/7eWMJRF6xtwRtXId7WA46t4O3xgLUW1LV0UFgJCD2lOlQIAkPVziAUo6Ex2IIOgABg39D4roDC4ytU+nuilAyNKABnX+yFHZ0BBzFLvRRAPK0brac6i1iKc3XquzZnD25r2GMMvfnbLk2V4jmMj7RV2OAxNSrSq4VxC7NKgfFzvjXvShzIQHWCbstgD0mleCJwvZfA0AOa3F3p6kP2RERkY7IrrGuyKzCAifEK98mm4OCwHLPVfgxmOgkIO0bHTQ2GjR1Ct4aWE6I0LOXj74bsERCkv9/5vgkBoXRwGPbdBwcVoIgZLWUx1qDVXgSGKqZSiBSUpYZ6gMkdmRtH20J8xUG8BxZJnbhaBjXDL6npwORpf9VdxiC9vfxRSZ/5G97cxwrrd8Q5kI6dGAMZRFYfnkHeS2AAzMUVakR63bcDVi24b0vYFVaCQi0W2bgmt2QwhtH7UNbOFAqNjmlAb1SGXTuUbren3+T4hecKpd4DgAChlvdTm4Pdck1w0F2A2oJDdMgDLDjq4MDQCAwFxaMLVQW1mStzKW0YbAWdAeTBn42TwWj8Mg6lxf4PYQ8RDBg2gG4LkCl4SRt2w0U035tLkY9DLPUziaR7X7oRkmABuM2HF/vI07+3wGBsoYTba23FGEM9DCrEWsuoQqw91DmMjWsGBo1gprLqGLdwwqB4dahz/H6HLTyDjMZPM2SiOABN50XdRmUW2GCzakazYjGCA+lrOVpQja4CvfNIyFagpaK1zBq8RJ+O6gANjJekuKW2RUjf48ViATwIyy2ycwsiZHrvt4kxvMvUGPU4EMlAMEU+EiTCg/EhGx/rBHsNZoFuDGpKtoX7ZmXBjnWxcOfEFiI5iuCQACEbGUtSJwIo9twkLyTh7cpU9qGnJxWtugULANIY4DChgwpwKFju96wOvTEqXiJu6SxLXJVQ7ayBiVaL2xbmkmyzfeFdigznHvvYPlz4YPzkmoycM4GkSmQVI6ebPBZDbwIYFMzSM/EUBBw9D4LGRYtVOOBeFw9/Hm0LDGp6Ww+496zJARRyHkSFvaeXoXbPg9kBZlrwyAvLv+cxRLoVnC6lBmDx87puqiUh/t752UbqlZfAK/TW7X5i07lNR9XaxgCowdpdzIpfirGEdvD7uMD2XQra6hfjvFtEsVYzPi7uvqRUFYuS/AQB4r0SMgF/T3Agg+Mz0mVri3qI3AQwAKNae1QkQGCos2VR5iKvx3aweIV251GOPVbhOKkQeiwQty0IQSG7IKd06jyobVA84iYPP7E5SBpdhmb102aRClpS7EFiDKHT7gVNSdL3AauVRZWn6FXGTMBA0SIYi/2O96GqxURU9ISr4jMW1a0Ky7E4AHqEZVoey5BHYbEEitIEtZjxkUBAtYKqxRYgCvLaJ7l47GO8EyezMqfzyr5Jt5tZ1H4jeDfqRHhRxZlOEaBhsC+ML3mSIfJmgGGWoxsX6Za8d8ZAtnDUJeIW3jpA3Ff7b7YFYwq1puhGeiFS9OLIEk405pntRz24Bp7p5+DAgCSezydeILWtJSAIzwM2QBYBPSLnDZuqATDBGlJbrIK1AUBmDKE+rA4aq0AOgBwBPaRQabFQabKF2hRlqRYE5U2woEs77zzYzfA4qZKuktAjcQ1AEBB6JKRdsMd+bR5xAyDT1h5BCWg8xD5nvBhAsK2QYX6yPpQmE38+EO3uy0fKTQCDYmTB90N9BYt0PGKJuAUmSpEt0AsRnoh1cbawjHYF1lFYpUc0ejTjWHhFx1mZvSe3+cQ9H8BlR4XlQ2RCk0IDHLhf2LYKB2yKYgx1A6Frmplgx4i5eNsjWnFHsikhHVeaotQGLcXAoDqhqHYuo6uKViVYQyMTO0oPlfYiLlUUa1GUZkFVOXoRGMHhIUL35jlwsMFk4NQavSXoQEEvxQWJSHMHCJ0RmQiT3ZbnLumKc6roBqBCnRhUvC1baAd5/42PM+vNKsRxqsZEdSJsC/WwYQvrung1JgkVojCVeh3ZQg5aitck17LWYb88yCHAopF8ZYPeqiaFJRnoEblAClTqHc3cjDIABFUTFWxDpq9qtNs1Cj0VVoUBXtGpVEXzY4sABQKtDgqrGyAX81DAbQ+MvlMpaItdJ9d4aCooQOQ45Ju3F+yUt50CgGtTr59a4OUTlwAFTeuHIHImlGxhAbDK1f12lpsBhixHdKNjhVhOhOdGvGl3Q9zCzBbWuhjNrMUWFdnELCTmkFSKnr8QIzlkz+2zV2hoL55h8DCk30mdvs/xATwedkCCkW8ZINbONvS0dnxWRNVW4PL6A0zwkqZARSRbQZxkrIJyAHRVtENnDQYS0mltgQU9lYLW1OIZnP5SvaA6sBfVKDts4L0e2E+RrE5458juSWxYw+N1m5sAhgYJ9YFioGA1FxpG9pDrLRybuSk7WzC7QvNVhqRKClqSkS2wPFtiDkACgaROXHOT88wfx8mAwHJrPvvT4LinBQCwGgoKRFUmr91IgOjHcpWEByle/izZCwQwe4UDgDDwKZX/EsDAgdEFYklWkGL/UZwxqKdpw0Ahs4Yi0IMzjMVXv6rF1I5m7bIISEvhjtoNoht2cEporOSx+nYMBX2Lgw6wBROqF/HdbHzc2WaPzUGvJXUi2RrUjZIQBAuzxvn5w5g866Zxgq3RI9ud2HecNYTr0tWJoUTfh+CVyBL5+lpwxBKGR6ZWB1tIiVLH2l+1CrQaW5gDmhifELEKyTPRe1Fvy8kgkVNeinMWqDSja7HzDzaHtB/boZPOap4KardTXUex8zMHYGjWOSMkjZyqIzhUmP6wqhkzHUQBq/zUWOBlsUIwFgUJyNEGkCwIxqDVXGitGeDYadXNIJbGvQcKOqsb/dZcxSTOqfmDIXLz5X5bHiSXbAyXfgt4H/D776AzsIZsYwhbA5cjfM8ZAzCGwGY1ojpA8P8bL8SyNlvTcm0l2EJ1PbbVBepsgUZGgsGQD1E13HtR2BU7aoN0qp+37UneL2IVeEyyBybvLB2Qdi3I/G3TmK2ysZLgYN4Nt7A/tpzXFI2pnjJtsQ1uiPRomoYOsLqYi9JsDN4x125r0EUBhkp74FMtEiuOVjd+Fr9Jc/k2v2HDx+fKrnzvZIc1KDoojKoE3v+0a8UY3Ug1gqrD/ZQPwfBnVmjKbIEl4LHKNvyZRVfmVOrInDw94DfbT+037NJpekSoaVIFVAdbhbSt7cK2+b6K6A02s/eVpmMWmRa83bZqOj49HmH9NQYirQU4qLidAWbPKECv/LQAZemsoUQacAqVPjg4eIJVc/VBVLCU1gMyW7FcigeKMczz++SCLY+1URh5U84Uvi2t3J3Yx9Wuy4cOXIGBbsusoasU6mrvHAH5ULkJYMhCNYJuypxenQ2Pb1Mw0+psoVarBUAXZXZJRi7EmpKiQpWYajZeIVe5K8/91kd0Nnb2ZegT6yiuZtSuHigAFBnBYZZgFXI+2CnHMTSLPdBGLtJMl66ATUfGF1TN21CKoh3Q60UuXpbeS9BbjIP0UOnGRX26Xm8JVsBSrrx5Q9Ml3JXnBjtVEXG1pTj4tFbC1nANWASoJBfiENsQOJxo4rnjJhZ5+qTp/zDraFcZQoXAwBoabVSPkJsBhkwPyR5Y7LW68bFBIgTasih7bsRaFwOFwejIKEcCw8gS5mrPQLcp5Oc5hCZnmZ+5jtsUACQ9nBwPkdSKzbF22IkuABeG4e97NFy3NVCdYBHYa4SAwDYSrEKdQLMs4kOxbMziIdGLoqyeM0GvhLfVIiXFQ8198DZmXxaI3/AGYLngjty7zRHg9AS1ohSqR1s7xm45jSbz43uaHeFaEWzjGfI5mSsxqxFMqnqE3AQwqDMDYCzAkmMXcj3H1cHg3l2Tx1pQm4U/ax0DmkpUZ0pp1RHMlEAhRxHC9fn8HKYbvEvTzGY3fBxiZ0pXLU6pIvvfdcMT4GHVXlxEF9oE3NYwqxPXiqor/L2t5sJk/mNDOSr0YFOU2RcSbRXaSdwQyZTsg4GGJDsDsy1LsQ5viVVmbJhViT2j5CdpY/gk4yBotGU4PdQng4k5hDHyQ1Al5gzKe+21HWdPBBlD1TKEPzc1yorsjpzUhbwOhCiG1zzjX5I9FjFHPo6fx4Ipp2jeXr/L61Lw2LsFWB4rM5g0YwrmWvD/Rb1kHGzNCr6axTG0Cour8HveUtal1aV01lCAFkVMSlD7W5CciLr9rnMwTc8hgEK7etErp/T9MG9XDAva2ObcQR7YeFdRsyrxWLkJYFAg1hZgQFNz4+NcEv5tSpZ6ux4i/HlTCr4KSpVhtagAhbTgy0Ml5yxstvHYeXt+zlP/l1MWs4QMUfUpkrx0ZDgMQnKw22+0ewlQTG3w93F87pMvKtag2Gmzuqrgnhy2qVSYLWFV6EECFCxWJFV4KiXOxRk4swaGS78r0Ty4Y2NXU3TYZhu0ybTNDbjhKui/AZDS3UdgmP3hUiRUh6HvXGIpgpRlaRGQDwaWJDcCDIL7cGAhYhf2qjO9TclS9xG34IvRcs3JKapxiFMIpN4O3EuS99+4NslA8uCfgWLK2twAQ554ZNqP7SZTqNuycr4sUz/9Unxf31CyXuN2hDT4JQPBXD7O6zfoAgMMByRpGmoFK1+1ZXoGKSdFazH1AyXyKIAGST53FpAFrOITQ6efGyxaKxtQ4CDPBWGCRTQOeunAQDDYeS8EC2ADFHmpQiQ7E13cIgkg8u8vCfvOh6BKqALHtGYEjY73XDPC2cKxLb6AzMFLwhesXnMhBzSVdQsOHQzO0O9LCLsDBkB/yLEUne8bdR4plxhDdJDRDrFhCAESW6ZwaewMUZDpGpg8te/unKSZq1GH1Gx1FsHwaEWpMqgTwRoW8XgsGiGLL1RTIFKHpRAYDckgqLz9sbJnI8j0n4bSMBA7C7BtiRFklgB0MBgmIDnJLu2/MwY/tGCMst2t9Twfg00YWMMHAAxwWwJlzKA8TGxh8TDo0tmCF3hlQBPDnwMMYv2H0yrEtf2MMzYwso4YpBkM2rR/qCHT594KT4SaGpP31/E6ItMS/Tx71xYdjD5w7i8ntp8SgmBVyNJZg3iWJQRorOq00tYwqxMl0JSJP60JZCEA6O46KePM/jQD4B5LCBsCgSAGuQMF77OtruM/9sGvGNWFPFlk9SFO6t8DEf1qKh664ftcGH4CMsYyRNXobIh8pNwEMChMfaDEStbunmR04xjQVHq9BY9biApN2ehY+wDOVP6hakRvbK+VGAM1V4FO39n2DOsTg0ifIzQaSCsxT/sNOqeG6nCOJaiIVRNmTAMt2U0AhkkTIBwcuF1nxqA9ZkJTkpVU7etgphyKU+oEqsSxm5edL2WMR6gKUx8w6uDPKaFGTKCgrbMDVQAx8DtQbABBZSr44/vk2X3nMkQRVZg8MsXAoUz4fM0tGNQIYx4vFscgIn8TwL8O4Aeq+vO+7VMA/ksAPw3g/wDwb6jq/+Pf/RqAX4ZVa/u3VfVvXTqHuouSkhmDGRzvhoCmo7OFdV08g1JijQiyhZM2BlwBCico9GxX6AlYGuxkNAr6PrM6MTOI4aAp/yHLYLs4xTjy8bjviWvzoKdQLSaWsAGF6dyMd9Dk+o3VrIqxh+Kp2TKrEwXmuhUbgArLvCzFdPtlurBLyVWPXNB5uBy/6BEsMiAkw2IwggwO6tfnQBET0RmHywAa8bw7OEQd+4dcDx9bBMs96Och15CN/xTAF6dtvwrg26r6eQDf9s8QkZ8F8GUAP+e/+XURWfAAsVWsZYphKJ4wZR6JcE+SLTSJpevzytRjLUfdZQ19eXjXL7kmYLqhOQAqQIVqg1c46oNDfTk7yy+QtUF8m6xphq2KWFoubZPabJ9Vd37X+vfT74cXGUAYsAS6FMu2CzuGhIHRwmhL35Z7BZ0FLCrrr9zeUpu3p9+TeZ1PCTeyDyRGQqaMy445XddnxmQuIttLuwHVoylrK/HKpd9yEBSL1A7Rlzw3D+7PNlSHmkAhXQeapGsaJyNWGbf3vm7JzqsH4c3eM+ku9EuiSaugncEZSGM8ySPsDRcZg6r+9yLy09PmLwH4l/39NwD8bQD/vm//DVV9C+APReS7AL4A4O+cO0dzFyUlg0LPiUj5EDm1OqkPBgr7XolM5Wbj4dWSjX2aQYH+/MQQGr+fGYPun3+u33iuLFfT4fuTs3scG3Ht1oF6OndUeZqNj92jmM6bj6lxP+CrXKNYunVZgbKjSpg70waXCoxTlh7bMMYJ7GdVPrukGISsQnTj4sQSOGi94E5WGXo1sKRW8P+p2TueTWIKQM+raVO/iN9t700YHh0cpF3RN07IY20MP6Wq3wcAVf2+iPykb/8MgL+b9vueb9uIiHwVwFcB4Cf+wkeDKsEQ6GNbsPJ/s6IsNDq26lGOjUyBr8wW8gNM7OAJ0u0K/G8sIWwNa+suRc7sc9RMsI/eHrVCB90G3dLsnusmcFu2XbCTFNl2hJRIpZCo8BQAwXiFbHzsrdq5fu2XUhVSGiDFAG9VIymreSlKFWdO5p3gwDJAUNDkIQpbi0I9pgGgqjxe3zMLWQMx0jbS0DipCwSFGPDSjc9UHbzqeGeTnWnCr2lwSUdD0g7c3sy2wvlyvgUnQSYd52Qo/xXy3MbHvebuNk1Vvw7g6wDwMz//p5V+616UZQlwGGsuZKNjD32WlDnZV6lm4E0Kfd57MPx8AV27bQHd01G7ZR75/bBtmnp3yrwL0EcC1YChfZPRIO07h2/vN54zksRq2DxadJ7ZM1H67+z6O8hZrYbmA0i9ZoPHNBQDA+ZJcJCU1UrCaQXgNDeiIZdch3G/N+cVq6hiPEY2AU3JHak+0MkYCAqhNiRGEBNO7eAxLnyMzhiAgTUE5pf+HTx5bbATZEP0oN7mD+MrVAd9qIWiy2OB4U9E5NPOFj4N4Ae+/XsAPpf2+yyAP750sK3xcYlQ6GAJQ/VnD31eS18ngipEFGGZdFzOTDyJ+OeH9i3trx5mbbMlaAfIgFAnVQIAZqCI7WOvGYKPMmjYl/69WhgyvGpT8iZEJ9zpHdxnSLSajV35/PkamA6pfp1iqdms8lSqQo+KctefyZDiXsXAoWgHhqT7x+1wQySJXkkj41nyFfZmB6oSwRIQ72kLyANe8vbMTuv4P0r6TYAgDVbTAghKIU09IY7eIbYvtVV2PvslSGYL75gxfAvAVwD8df//m2n7fy4ifwPAXwDweQC/c+lgClMfABqMSuRFhDqR6zk2qhHohkZmUKZCLHPCFDRTdf//kP7l9gXRiS04KJS1WfAP1YeqkFrH5CSAU9a4bWYJSxmb5j1JybFVPTquDyarCIVuf5gBovFu9+uJMnApOjJclhnAeJLa/Nqan39KzUazJQ/uuhphFadzsFOn49q0z9g+kzevCymeYBWJVc+oUoQaMRkiAXQVwgFiGPyD7UCmPjYDYO97OcaAzyIMhvDB7IxWWlKx+H82Hu702wCFBA4vBgwi8l/ADB6WDxcAACAASURBVI1/TkS+B+A/hAHCN0XklwH8EYBfAgBV/Y6IfBPA78HWJfoVVa27B84X5PEKAPqyc+6NYF7Emus5TqtKjcZHxJqTm1oLTXulpEdMONkjMdgQMiisiSWs1YFiAoY9xjABg+ZUaA9BBgBZShgKGQQjXqodh/MXNQQ6AdZRG4ZZSdzesJ93PNtKmmWycgoFYIYStze4V6isDgphcbfvdPFGNR3iCebe/OKGSEUwF1TpKgTbTDaabQcTS9isVUKPWKiwOgQdqdfTgJln7PEzfbqZGhbqrYzxCDu3yER4oAQKjyRW13gl/tqJr37xxP5fA/C1hzRCMYZEM6jpvh2sCIurE2srqJU0T8JvPCD2FPCzyWm4Qva8Bj1giQ8/R/0lpkAVItx6bWQHZAuzIbQIUPq0MFRzUu3f1Rbp2+LTg6JBSrEU6WI9rVuld67ruUWtB9LoqpE3opHIFoOFC9T4WhXqWZfwlOwSZOqMHnRNk9BZQHaDWum/nomrtXRASC7V7t3y9q9bhkBAoFrRCw2PaixFkJ6pRzXSzpJvpWkSBghm6vDBHuCdKn/Rg7InpwDkCrmJyEdAhuzKbHikGkGjo1Vomgw96UEN/5OQXp1thXfs/h7De6HtYm1WL5JxB+HHb91A52BhT3o2Pu5Mxy3++EkFOaNO0/Z+MeqBQik12is8b66ZXg1XH87fCOnZmKo9+YoZmHs302dFi35McQwBCs4cDubbV4KsBzyNdgb2aOm3Ru3cmTlw4IdRMm2z7zM4mMrQaLhu0iNmPZYABIK8fOEcMMe+tdP/giHkeIZpAiBj7cyB+yQQc8JWIGhNLQrSw0yUzwe9nwrYnjMg8UC5CWBQnRiDA0KsRZlrLjCgKVVowgkwmG9SxKTPNy+xjJ6PwP/+AEItSQbHCFRCZwythR5OJhHsYI+KU0oZp3ca9wADCQcTZkyG6tEAEdZnbJ1BOKUPF+bMWk6gpNWX7DfQ1n8Qz/VJ5y074BBu1XyvNKz4VPd0gS1nR9awaDxDbYImLOTSYjBInMLe5YCnGRC4X7Yh2O2WqPLFRY57UR/rT6OrW8ZgucG+kCchHRiDMYkOjOP95TP1zwK0g0R8S0uDuwEoTUzdWHSo1IQmXaXI/Xn+/Ei5DWBINgbADJFv6wFNxbwRaREZrV2N2I9RR5rps06RzncuCky72rAXryBVUY5kCVQh6ggKzT0T5IXzoCQgDCb4NqgSg6qRNks124Oygg+9C6pQLN3mADcOKgIgAAdH5YVOA/uhFaYnr4V6YEKuIRHu4ikdvngFabr6ZsbAOg28LQ+xMbAIDFUHgLfd0/PDRuWLHK8TK8jrjyTjdvdGjFG0o/2h2xVMlRrblnX/HmzWoc+OnW1NBqRFGUpu91Z5+6eJrD+P8fND5UaAAZFd2SDmnlSzMdRc6JUuyuxTTvSupyFj/6YkPW1P6NIMUBjyIFIYcLItdNYwgUJiDqhj79Ad46PRwWl7DmaKHAdBqBkRmwBgZgzqYc5FAyDM+KjGOvaeA/M0KNmP7nT/7AK5Db3KE20szhiYih3rTzD4jAFPk8uyNTFi4gTJNK3iVaV3VCx+pnrRioc/A5Ek1ejmTqBw9KC4+2TA3vxPs3/0tfxep76YWeXUSB/MMQ+Khh2jenxGttm0Q59fpMBS1uHqBZAA6vnUCOBmgEF2jI/MqsyxC9QJMbiPcmzBgKDzeS5ZaPPv6/yANewLwhyIqoB7Iwb1IeUUoNaBGTAr0T50IFApg+4viQ2MTALhrhzAgqCylLD4swQ8DV0MhNrNnJzvk3gQzwJE5Se+P3cPm3VgRC4FB1aekV2VYBBUI4lx1yHLywtVAu0YeVGFQCrAYseKY9PIyNwarwe63AvKfWYzHRBy7k24qwkQAQ6dQfB6soqxcU0Dpsb4vZY8CmOAa7xXqlk+RAoErWiETQfLPdHvHyM3CQyrBzYNiTGsAE2rcQaH7EveuTn0FwsZW/o/7xvoT1DwEOcOChreB2MHiSlkm0ICBw0ldwKEocM0IHf6HB5daweBZUkMwlSEUC94zKUEYxA3FkZobeYEjH701GwAHZweolLQDgOEPWMcKBKZlsVZQ8Q0hLFO+gAAIKFGaI81EB2SRs/VZwj3p12gUXIaGpN7m6BQ7hNLOGqPjcnLDTDPxW/NrHJG7kximNlO1RsHdBuDqztaRvtW6qh0Z8bPxdqpkvp7OsXGcP4IuQlggHbLMoAoxHLfGNAkU1n4TKEmQJhfAPamOHU75N4NHUCBIBGg0IDVAEHWBqwV4vEK4Y0AOHXtM4QZKAC3IyRVopVxMRq+qbXza5EeXu3p05qCm4zOF6vs3AR9HUVgti/0aMkL6kI0KOVlOLOA2sBVp8O9FqSDQoUtYXfos3MPJ1ZTJ1QiQtVbFLcsxzPMoJBdkh2TpYNC5D9IeB7KEbbQ8T2wvO3xLxkQYnGipBYMCx9Hv9NgC6I9HD5uV87iDSOtA2pbvP8SIHKIu98JN6kxAApU66hGpPYMoPA+A4MCE2MoWNVAgUFNkTA1LVSbQ1QzA9jr3BeTShLYQNmpW9Disk6gEAzBQSEMju5BaG20L8zxC5o9FoI+bcMG2kCbvRMtZWQQlAhxNsOjffYLrgouI4srkuA1UrI9IYwqhLsuFfs2in6N0tWvpm7tV1vrskgv+9bg/+03UBg4eIKVoBsSF68J2U4Awrits4UMEB0UfOAfBcs97PVWLXK2JoCoiuVee9q7IrwH/lDsntO25dfrDUUObNsAA+8VvTiq6XkXLGS4A8VF5NbpnP36BBDYkxsBBsHaJndls7oMVSW8ETkhZ4g8SwagjXoQrMEeSDCFHRms6bWjvrQdUPBBL2vtRka3KYTqQPvCzBgaZ708owDIZdSZSxA7+PxZgbw+heSORjuHO70zYdJSks3hkrHFhatZBXaZG/FkD4wYEPVQ52Sb8bJ7kF4stjjRohoYA93BoBVEgZbmVaTLmXJnM1ugpyOiGhtClcjrjSxvFctbYPEFiey/mkqxuhdq1VCR7F50pmAbYLYnNo/ua/TP/T0Gw26pCg0DgsQOkuM5xKJ2pcBrazrTwsSYn0luBBjMGwGYZXZVS5qqjUFNslu6bTfAKdGpUzfqvGdCgy3kYiRYk1tyrQYKdcsaNFSJzCJOAELqLDZwU0PKaHOIwZgrPUtBWjnR7A/sca4S6AFuh5hSePcMkA91V+ZZK67J/oguyXujbltAVHfay2eJhCV3yZkaYNWkl4es3By2BXsFKOTIRaoMbl84vHUgqIpynwDh2FBoaJ5ner5vvK+cHKgiZKBPauSsSuida3e22K/UzhyicFBxe4vHfkhmv3ts4YkM4jaAweMVKAx/PqYoteGhTgkt2fA43IzM2gU9T2BHzcjeja1L0kGB6gNBgbYFsoSmsMIDLrUOA01zDAO/71+OA1UL9n33izWyiNMHv8wiEB6PrsxiLEsjGErCFnCSN+XVspSf0fMnmEsxS5s/N8ikUpS0hF0vEpt15uxetHbK9Mj2wpzz/7h9dH0mFUVclehGRmC5N3VheatY7o0ZlKOi3NskUO5XyLGOwACMqtzE2IbtHPwzMABd9awNRc3F2/QANFi1LTRoKX7PLCO1VISR1p7HdjJ8DuZwE8AAdN2xwWLm6Ymg4XFjWxhmG003pqsDs8REe+LGZdYhPtDDNbm2MSkqg0JtI0MAQPtBVhc28QyDKtEmt6R1ik0ba7XBWtFtEtKACuiy2PcMAKCrMpKdxI67XDA0UG2JYByPhfA1KmUerfxZ8HjzgtB7U4pCVztGcWDIYcOsIs1ZXRsMFF0lQCtQaWhtXLVqXszWcJdrQiBYR7df+HnXcdnCw1vF8qZhuXd2cN9QjjYRyP0KHFdTGfOz81iQIeQ5D/pokG6/834QBXXRYVoY9XlvNqKFBW0KYuHgIf17ZsdPZAqUmwAGheBt602x5ecs0Km1HLuA4aacVCOuPzGABASKYArMg4h6hxuXZAKFVrvKcI4hzAFMed8yUc9StvtjVjlSZy1i7fF4iF7C3kOamZ3EV9PNojLPKeJu2iha4+XmrYI0kMPZuwG5D2hL/Uawhmw7uCYKshsckdSIMTR7OSqWozGE5b6hvJ1YwtqA+yPkuG7yWySreYNakdq2ww4HLxXZ5bpaCPxxhZTimC4OCAayZRG/bz2snOtUjjEVaTzsTI7Xyo0AwynG0I1HUSCzjhdPeSh9yu5NpgEPvucIWrni7oq4vl0GA6JlSKYOtDWh7x9vhylwmzBPAYDXhd89hKp29kA1ohqTkMqLz0Dk9P1U4BNHZ0F4JSwIKwMh3L++U2IOGIE4DMZ98EpzQKjD7p4/ZawhvAw4ARRJfQgXpQo2FZZ4TvdChPqYbAbBvFgod+d5AJ090EU73BA+45LuWTAH/93h0M/Dwzb3hiwNsi4oS1/xiwbcEmnaHyowqLkoAUR+RAQ1pfp7OSwV8/8rRdINm0Ofe3YcZ7rmD7ttB7EIsCwG2BXWEIIDpbTh4eiyDLPIsCTc3PlKGb+P7YLBrTlFTEJbqBnmnrWOKLWZV6FZYRXUVJlBvGAKPQ47Rkl+b9dzShfrgVRc22D42mYAG6gT48uJS4O5QABlMlgTN3PY+Wf7QndNohsc2Ud2jNVZDY0JIcWFqACyFCgO4/MXsRk+f6bXa1Adt2wxcmgAgHa1UqCHxY6Zcl9iCYJqAVfLUSK2ZSlqN0dGIBgCrp6gUtwGMHh+BAA0Lc4YPCJsB+nPumeSh8cPPp3MNgwJMMlyXliaLXTe/rthNpgzFn0NxsHF3woGuv+ckgfd3BagA0RxVYz75MpLKBFeLR40pdiCwyaHwitLKyRciwC6+sKB4u9n9jAHohlIOOjwvnunZ3g2RK1eQ8MIHOE39I9IaoR2tkDDY1cnPLoxTQRhICx+3AXo+c46XtOyvcaBDQD9N0omqpMq0eL3WEov4Q+/D8X6o1YBjoAWdTwieE592UFwjKB8nNwEMAAWBg0g1IjqbEHdWm0PVLZ0KfmWgd6JuUkUJ6Ick/qQWUIlUhtTGB72CeovS/EHPkWdFCAiitTZw7L0WeVBqoR0drDHJPZSoIFevMUBQZNaITAbinVIH5CLjMBKoOVhh7RsDB4KloSL9pRp9nfDZLfl7HiXqgMCFLTKqftFe2LoFAGZ1QkaHtv0CgAa2QKffRTvZVMJbod+LzPg6SLxnFSwifmYRVvzCa6zz/B08LksyYMEn/1XRSkNKsXW8Ow0r/dtsuBBRRuf20PlJoBBNTMGNzqqW5fTg93mweNRFz8nubDwSl7YZVhgZU6djgOJ5y5UN/DhNEEQtz80DRVhG0ewY0fgb3ePJ6MXhJJnpcHO0MJ9KSLBGBj4xGCpXftAyqGIJKv5ltAV6jT8lJ3BwDy7KtP/Cm8venSfjwBt6sCogzdCMKkTLOg6sAV0W8agVhAcJnWRZgO+KYj7aO5DT2bxz6ZmseJS/xzSigd+lVAndEksC36/6GhSAJVFdt2zUxTLfT+k3RZJ9jDeX4znfoTcBjBgzJqrKqPhcS7EOd2Ex8gWHNw1SbZAI9RMD3cP5ggPjK7Axob6cZqHJNNtmT0PezaFGRCo1+Zz7AUlZbBQgdYGEQJEM8bgOQvhWgz9eGcwIw1ylp5PABHNDZomYVXfP9j4yqBgFZ3U3auO+5NKYQxhPByYYKXTOQJ4BHMwlbSUB+HqZAxOnuDQWU8YGYuY/YQuRKGur/1e+eeoxt26OhF2ANXOMk4YpsvaYhEfwGqVLODgdzqnMzD0vI7Hyo0AgwzGx7UuKZtSOlNI8QsRs2AHsPsT/aa765gfwS5PqhXIWt2uEEvDtTFzslGPm+h/ka41cIDv6fqU8FxoDGypdRzklHlbVh8SWIRdgEZPbd3gRfARtQ7M6/H7oAdYud6leDHZzo4s1z+f06872LoMABHPMQFFAAkHSQYDb1ZkIYZKId6pnRU0PzdDgnks0SFGy2ymzI1gn0lsISaV/r54OjicMcragu3woKEiZLXI6b7SnbhIRCdOzpEpNN+N25zNve/NQBEqDSemohAHhXBYJJsJxMcD+nE54T1FbgMYFDjWVMGpZcaAbexCViPm65edbTvCDLiIWVjH9SAkqxKzQWlzsK5vDtRw9lKE5yKDy3S8c3kMjFGYjHu7hkcAWiukiPvLlyHAKsDh1DF2z8/j+z8HiPh6MMZiYBO9bbphgBvPhADMawivRCOwEoAQBtFMVIY+4YwhrzE5V2/uCwS1uAYGmetSgh2YsREBAnoo0Q7lGqDzpQK9XHwVyKKdpQYwIBL1WB0qbqHbJfIBCwzgbLHg5CZVIBK84hyPB4ebAAagxzFQraCeOMwms+HosapEdJhEI8kWVLt9gch90kgo44MDhsEg6B0jACDpkXPcQ7CBaV2I+G4GhWXxTqHJ3GrnUm+31jaM5zxUwxhZm7MeRGWgjQxtSdtnHTmo2WR45E85M7qBLGwNuTZDAVDFAYHtRKgvnmBu7GAGiPBGYLTQz+5KB4eeUk01wC4gBn0R6EHQFukgQVeswBaLmQPFJLNX35QZ0epsa3ED4gpLFlub19JINq6GMBJLUzO5FIEcBBo5Gf4MajJ+fhCMIbkrAXTDI5HeH3I2Um301D3mYAff0LohnJRsgRmTtBynKs+yBxAR8OMnympFXEgflPBY92HnDaPAvtdhBoVTXgsGBqnPgFRvqJujjuDQei4FB+tsZ9ijyNEsxU5b034Ogr1ug0buxRCPkhlD84Qv4bN2gCj2vtsbeOo+wwtcxVBOJK5ShNoyA0Si9HkdD7+fZAptEWMIEZaMBBCdMQz3iOwlAQOvu7g7tKwG5wUFqlaOL1RXujib24R81a6ianEPVSJOhMAw5HPUD4ExaGIMmpJfHGWH4KZsSd7M1v1ZcPbqvnWMU6GObIEp1TNTGAJS9oKcss0BGEdONi5mEHlsaMOgt0tXRfYCsJTrO6ipFADc8tn7awTmeDtJb5oOdRsGcBjiG6b2zcB5SvJAjeeZIltFILF8nYKBVcqsSzuBsST1feEXFdGOiIFIo+Zg6PTthROAV8HCgS5IU5PUQaEdxGpVyggI8X5iVHFvBsZg1xl2r5ImKLJjHofgvppuJdJsEqkKLO56Xjhxpomr8Xzz4HiY3AQwKIC19lkwMipV+kMMOjiBwgPVicGlw4HPRWLWmHLBaMHeyB1Q2FyIbrdzdk/sQcOA2MaAqNnAmGtAZqawcw5TJwDL1+BLoaiQZengkFQ2eiqkNIu843XrEM6EjVowhWvs3hOqv7PXoqrZPTbggFAjpNh7c6XCBlpD9AdTVTRcl0SueZm5wS5Fg2P8zzkxPghdnQp3qQ8+gkIjEPj/loAhq1fBGIBkY7BJrlQYkClQmLnqDIf2C/uhWuwJlw3wXBgpJbxTASLsp2kSGia0R8hNAAOwzZIbvRGjNdl2ehggDGcjTYt6C7VnS3Kf7Nc+d4OHmUIwjJg8eAbXJKn1zgibQ5yBrkLwXCV9Fh3azfYqmYIPcotbAMRZA4Be9Zkz5uKrq+5oKnFo0uYrKkFZ4/cOohhYQsMOQHjAUOOgB3pEpHa2JEwQcxdmsAVJfUi6ykLbQhgh7fqlNrs/BFj3PDSval3vJKkRGSQuAYPT/aLOehD2Moasi4qX03eWQjVKNQzG9DygFGCd1IjMSueoykfKTQCD2fu6KtGcMQwhrGQJRNhLoJDUiKHzRXwCaRw/7zCEJyDuTQrViwYHlPEaY1C8w/aEUY6VpD0qmMyBNgoyCBoHo0KqqxwRLal94A2JcolBDH2IgEP3drhb7TQRq5BtCmViCk5uh3VRJ8YQgOZvtfgk5zYUXU6oZnszvzBnJe3D97HtAwAGwNQHSkQ8poeabQtAUiWwd0PRYxXS/izoGb7kwT056emfBCjkGIWZiewZHE8e54x+73Ueel2H0tkMbRLsbAJEgPm5Yz5Q5izZU6pEKVYmXQCPxXDmQ8bAR0TbQ+n2hTBSZ5Ui+tPOs+U9Do+D2xeyTaF0G0MHjWRjuOYWETS0g0+Ax7l7TJtS7gdh4+JgSID5RLkJYODS5/YevSK0U00MD3ULBDN7GAKYAhi0g4P7ePlKDenvz+loWWV4ykMogpmTD+7I2M+n0TmjL7/P02wpkGU57bIiOADGILg9GbG6P1/NPajuKYiZ++mdb2ByjDEo/QV+Flh/KMYKaKOwpe0cIHJ8CBmhn2MIqd+7JVNCFNwbsfVA+H/ZmYyAzhISY8jfKatj+aPKXguzL6TV2IffyjZpDmQoMj6/hWDxgIlkR24CGADYKtYuG8NjpoHoHSq7hXJCSd6H6kK4p1zPZLIUjTwPYgjZpTf/LgcvnRAh6od78IKcAApNa0rEOefz5s99vbcwTiLr2It21arpppDL4JfXEzkV0kFjbz0FIcCo2wWCyVkl6QilLq4hLKZmgAZJ8boRxQBLmX7MmZKqSbIvhLsyTTKDtwo+yDztOa8uvYlbyOrDnkdikuydkNRtzoqHWw+f8zEnl/ZuNamNy+5hchPAoApbfQiuPSTD424ClQsf9J4q0V+JKSRrdLZKd1fPM6gSe56Jl5QAhWTc3Oxyoj224IK/76BAqzlq8043XdMZA+XJhYNjh/6/1w5IQU5iSWbBHlZvP70ZDhKZzWTVYi78sqnbkV/zfSQopFyIHDdBUOiAwN/i9IA/0xViYpN5exr4GRRK8eStHVYJdMOwKuRDYQxcP8A+5Dj3jvi0M8w+4z16OAS1BFVlIgsQwU3ZBzyrFXsDjXkMnKnPhTA/QDYqxFy45cR6k6eOpSK2BsUsKWmrM6YKHFIUJe+FBz5p0LJnutakFnZvk9Ho4gNvtjdoIUvstSECHKKQDDojQHqfz7UHWNnrk1SFjX0hvTprkMQ6rrt+1tIkg9DMRE5J7g85jgXYTmi0wzxBbgQYPKCJwpWs91SJiQZask86lKaHrz1pJcc/sCS87AHCJTnHCC6FoaaYBmu/7PeFGRRyh9jdXybXqFwOovJVsqSl2owTSA7xDKTBp67/Embk50bVT9PzKObjbwJLMaaHwm9FKYK6qOdDdEoS4EC35VDcRyYPVPwstXtfTQPtCwSAxByGACZe3oXP/XeyUWPyf7303DMocHtiCnGwJ4ZEX8Q4EfmciPx3IvL7IvIdEfl3fPunROS3ReQf+v8/m37zayLyXRH5AxH5KxdbobAQT3/NhTsHD4WOv8t6bI/DR5qRNI7BRKlYVYgDoGmUhY/XKXdPXl2K9gm+srtotlvMsQintu/WF5TNvrOeadFxApReLuzJoswI9OvI940DOl1jnj0jz8AHQmT+5WNrAuxgdBhfSSUYol+HiuHSt/G56/ga3NfToyV114UZlUg2he6qnAFiY3eI4yGAYDzP/raZAff34zMennn0g6RaRN3IYgxwfj1Aruk9K4B/T1X/eQB/GcCviMjPAvhVAN9W1c8D+LZ/hn/3ZQA/B+CLAH5dRM63SpGQXvqgTmDQB/OWEm46HbYdYq7WMywrd7Jd21l0891zyVx0dAaMh9B4KWOkZBKN4KcEhMkAadF2PakIAGJZ+2eUrhp2AC8VsYBsYem1wa3Z4xOQPodtwbfH/x37wq5NrqCDr4NZDFgO/EmNGOIdXAZWcfbi077Db8/8MPeDbCSlC5XAxpdsXw+Ri8Cgqt9X1f/R3/9jAL8P4DMAvgTgG77bNwD8VX//JQC/oapvVfUPAXwXwBcutoSg4EFNJ2s9bgAjN9b+dTbQGcd2EZmcpNL6cnL+Uqfa8yvA4IkBJLsy2Bhk9DI85MFe+s0MhicqTdMwy5Te55KoO5DUvJIYQwDCzCDa+D4MlzW9J1uYgpyy4fqknaEUDLUWdu0LuUgL+iBPdofzF98Bh94Xey9nf6uMYSgEJOlu1iW9l2lbfj1AHmRjEJGfBvCXAPw9AD+lqt8HDDxE5Cd9t88A+LvpZ9/zbadFYXYFSp4hZsTXpO6ivxGMRsnIhdhhC70QRhtm/qtch5ck9MczD4IGzEdGqAWlFIEutiblKUs1U7PRWl8+D4AywGlZelh0vByY+dvpuPYM9m0Nm+zLSWjMNBuDgiuMQRSF9iL350d0YEEsUuNGBtsvXNaekVmsAcPkMU0oc4Bc9yzIMGDHSEekICT0ASxp+wmJ72c1+MQ968e1OJM91VNZZn6PCfB8T5y3rgYGEfnTAP4rAP+uqv5/J11g+7i3uSUi8lUAXwWA5VN/pu9BtNftA+WAtsw0z8qXdAaiBTvFCbaAFMcQEZCPMUDOq0cB+9tOSab7ey6qyYYwBL9wf7YlZpTm7kQ5bYDMDIHxDFdcv5UiS+07JXsFaOJ8iFX2bH0L33cVwI2L4a5klaWluy99tCA6jAdC6UJbBkZ3pU4vINikXZSMtJyJTA44PaYhqRXehF0D48mbl5rt/3WHJMzl8qKNszi72U3CA67PZTkhVwGDiNzBQOE/U9X/2jf/iYh82tnCpwH8wLd/D8Dn0s8/C+CP52Oq6tcBfB0APv7nPqcyMYahQOgEEEO5toZwMUV7Ezr3PPwOBNaBElvQFAYcDdyHXK70BDVL+JNViqw7Tj7r4bx7dRp2DyfQYlGPzKocD9R8JWpXn5bFtmkHTmXRFvES8/A0X6BH1j1QZx3aGAZiAogfE4LibEAr3BiNWHlJvNl2ZnPJCWCDoCGYyPCKvpPiWWbM4gBLwMzkqGAL7GMZEObPF4SGTevDXjw2s5IFp0GXWZUZDJaxT1zbjmvkGq+EAPhPAPy+qv6N9NW3AHzF338FwG+m7V8WkY9F5GcAfB7A75w9STYW0bo8gUM2OjLzrLsl9485hD83X8qedoVMnwkKDPg5pXN/SJJtJTNr0h7f8dxGx35+BGsIO8OqvioUJtuCjraF6BvS3ytSn5FNQFOwh1OSdHNNBsjZUHi6KtX518ZlOrMY4GT7MlMMD8rBV8LOUHgpogAAIABJREFUBsel2PYTr4fINYzhXwLwbwL4X0TkH/i2/wDAXwfwTRH5ZQB/BOCXAEBVvyMi3wTwezCPxq+o6ilSGyLDwiUdBPK2h0jMSlld8IKowrUo36WcckHu7fNSg/GxQvp/7jsHYjA6Ef45/Y8sxsZCLD5zVrcZuIW9rIp2MPtCsIZqLILpBvFaxAzDC2I2HjwZ195KHtArepVVUKqgOWMBmUw2El7JFoaMTtq7EmDZ6tvaVaf4oYOVswW9W0yFOBS0u2Vc9Ab9/j2HXAQGVf0fcFp7+sUTv/kagK89qCX5ATY4THcaeI1kujjEPTSEJ2II4KktAOPFZFYR5u/2Pj/Tw91ItjucOkeERjcn9rZgDYORgOYGT42BEYlVcHocAOfHbP3YYbhM4BD6PDyAqQGlSkRAlhXQoihieRJtQQcqhVvc1RmIBIM466IEOkti0dXJUF2OtvKTMpNTMCDNOdfkxgib+2JiMAYMGuBgbdZ4Xro4KCyLLU1wKGh3BXpX0Li2Bc+ZPA9PVStuI/JxenjT/T9PAafjZBmjHgkGug1GeuQMrbQznJI9UDg3+GlMzN8/lT0wh6KY90KlWMWmudYkz7V3P3yW52DMCVSRWNUAKfQ4aOyXax1EQpsPMlVAnG1ETUdosIhS3NZQxMHBfseFuxvQn7la++Z07lNqJtvTbVHOFpotJ9Cq12TkkpJktJON4eIA5PdUKwgIMYk5CK25n/K3kgKWygAK9a5EHcrelh1r5iPlNoABGB9wRtgrfieA09K0rQJhfY6cgMQWZkv8I+0KyrLjs7twBoUZEBggE0YoH2h0o/FzveYmTMJMQQBSqxsTW/+Oa2p6inbIQ5gTZ1DAWQI6C6BQpYhnmzo9PAHKPRMSD9JXVmJIdIUtzVZ8oCrQDnbiAliVZm9PnpFjZk7MgW3hhLG9Jo/0XI0taNHuNPKRMrsnB2C4MCg7KGBQK2IVNFYUU+3GUNoRDgXt0EGh3RXoQfoiwsDoUZnb9kC5GWAIRKb6kIxHFwEid1I++BTOG++n6s+DR+LFLmwLCL3Sj4NDpoDlRKd9V+KgyZgHQUkrVQGinrC00KMAwMGtOxc6mwAQ++UFanQpdi4a+DTdIq+LKIvr9yLQNZoHwOo7xumRiE7uP/x/4XqD1UTAW7Eisfc8OvaZxynGQMNl3pQid3lvyA7KUT3Sk1TI75GIpegXNzQ6OLS7gvaRoN0RCEZD6XOwhtsABkf44bNioHrXyGz1FcVY7HNmCy9tX4iGTaCQwSKrDy46JMXcgDTYzWymM3CR3FiyLTIbjQWMahq6jSi7Cmuz2dD3l9ShiwC6qIOCgWXxAeDeaTS1gKh4etr/hzoR/eDMfeT39MBUjWXhRATlaGcsqw/WTW2E9EHSQeN7MkEdVeYMDGQMrk6EFNjCzixff2BhWgMFq0PZzzsUk73SMHpKbgIYNiCnJ17YXmzouMM2HcGBs8IOWwDQZ769qMV5/cg51TV3FFWLC6AakUJUB5awiV1Is29uB9AH3yx79oATQBMxCLQzFJv5o41FXJXyqk+hxBOdyQD6Gg5xn+EzWxgd+7m7N4KMoV+bwIu9QsFaChHw4wa5JjLcIlLDBmuiL8Vr4JCMkRv7Qp5k8u3K95zX6pGfsgJFBIDfM1dfNunMe3EEg3rBvoWhPzLMPOxgeQ1NTpRidhNdrA6DFabtoFA/ktHGkEHiiczhJoABwPgQU0m33X3isw4PZlY7clq1eMzCwBYGG0OncJu48j2vQtgkFv95MkSeMkjO32f2YNPgOLAf6p1wY9VuUde5kEsGPL6f70eaSbOKEMIB722PNS/n73lYWv+pt/OYXGCG6IBiqsTaIFI6METzEnPAyBwkMYZNnEBc2/y5TwzmOu33qajbfyqBYZ6F0mHi+e7P1rnMIIBuP9IOCnO/VC/OEnYGB4V2J2h3CBvDUIz2GRJrbwYY9iozDQEi1wrpa3ZZ5sVDaWdwN5W2Fw5omtWHlAizyW/YA4d3Lee8EpM6EZ3fZ7lgAC6ZIdh++b9244BIgIT6zC2rL0Bf1E5d0HMpYEa7CvH6rx0csuU/rP9nbmUUAQ43R7NEPq2IwkApme1kijQQz3IABU4yOSiPrCkxlmEVqTwxeOanBTCZ4bUdgHoHB4ZkX5jZwxPkNoAhIztp1LT9WnAYEmUYq6C6VSOYTflSMnsoZhUC6MkwsFkOgHUkBl89pX1SXCWY1JRrxO9b/Gopyfio3S7AHSR9ztdNhpKex9DpK0b1oyS+4cVnAiCAtMSfRDoIGTecOfB8oU6iH368xvSexke1+2XqjB/cV4cK92w+xjz4si1JrIHqTMMKA2F8phc8TuoMUIvZFtqd/z8YW6gfSYRqz6XsPwgbA5AYg8rDmEJ0UOlPjSyBBigaHwMkDBQGb8RjPQGx3HzZqiB7qsCUS59nGfHrGX77GHAIu4F4Jme1+Hyg2xngqk9yVw4Vox8ieywDGAHhxH5jlW4OLAdyFBQ0NBQjF8kdPfj6hRO+xvebcPk0wQSLyE3ZaaMZWX1gz+xu7zYsHRhyfEteGzRff19I1/flqtlpwsghz20xttDu4LYGVyUyCDhIfBiMARhCok8yiEuy97DdvjCXLgtQeGhm5UNEZLd4p+aZxW0CKNpXhjqXnfiodljWpcCTqpjSu8kMddAkaOXtptB3daLoCF4b98DEEIBphkzvY8D7tiqQsMAVlNbMPblItzlwIvBoqc4ceO4RZ3ZviwNEBGSRqVGV2/F17pZ35/FypmzZAYls40n9TgLIS2SJKiQmkKgiNYHDBhiCPehWzXmg3AYwTIN/8EM/cND2aEfX4cI33DpTCKpOqvuCKgXQO4qMufQcgObkS6a0dipk7x2J368g+W64jNBoVa/ROYHALDQ25n3m/VyliFOLLeCqK8xzYhsBFCxuy6goYNV429/Yz8YueM3g4OSRbQ3AyT5x9pCTJwrA4FU6udKZiK0dWtTAm6GhDHDi2pl80fDonyE6FI2xPnWpseflNoAhya434knH00BnRim+k+zJbHTMHSaBwpgEwxmY+rZc1EFvSq4B8L19dlQLmhLDpHhw0FQDgVLVGELt8Q28f3Mk4vD5VBOzmzdcrKmPzKnr/M0sQ/1FV0FYRCcfk58JFKzHSCOjBzVFMNPBmYKvo6kL0BYDBT3oBgwMJJ7GGm4OGB4rZ1Ow31kjBGPevL8vpeufucCGg4eo6ZJSYX5rsoal7GeBflIei3csYfDMAOkuRREmV3leBcRmzuwy9BwjLkS0lz9Bim7PyWk/1Tmge4kuNlZ6iX+qkMB+yHmxqE8cvCGLFWuNFOm7xVOoBe2jHuXYuOJ21IrQEwFWeBIoAO8xMMx6Iz33Q0GOdxhaLA4AoTIkcNBFNoAwWK6BnslYYfYGlHG2GU72BMPkeyCiah6IBclN6oO8qsU4RHKVDdwmiAVrwrbQqGrwM21O9pmLy0RV5dpsm6a+w2Cvsw2e1IZs1E3GZnFgCLuSH1cPZcycXHouRPdEINjCUF8ya1szQ3qCvLfAsGEGp27IKYv5U2WOiASSilCCLVgGXPJYZEBwi7ohg7pbjMYot555TgGArd4bRjKqJI+8Tm0It0g+l3jehGiKDLSB+kz9b2pHaj/dgzrmaDAISMSWtLNl7DynAtK9O5lS++ehPoNq2CgiJyGpE5oAQSbWtimxR8kTQwBDNyJyetdFrBIVgeFusQQphj3feZTjImgfeZTjnaAtDgy5ulRuF5vy0OIlO/L+AsMkJ1WJCF55ZnA4VWiFrwwKhxQMlAGBQKAAlMyhqxJRAJWyyPZaQh2R7th/jDTj2qoCiZJvunlFFORLyBx5GiBFMBIbtFXNjSkFUix8GYsOLk11HSLIFYFhtusWsapNhxJqitmBu2F6dxGYnXT62bBMpsjzx2kZLOWFbYIhLCkX4mC/Z5SjHhDZlLQpbHMJ8CygAHxAwBB+6ogwe8c0O7kgByMU/dBDsY+c7GIFSnp4dPN4Bu88vn9Qz+xK/CSzMF9YwvNRtKsWRQEpkZdhOQYWNu3aRqgPKH4Mv3/GGHS0L/hzYGl225/32QyCckqViPgTAgW6usjnDVcvhtJs/bexLbOEQ1cdGMxU72AAQVVicfvCrEo8o3w4wIDEGhUetfaIgXOpAvI5WUqEz9K6bPpjGR9c6kwW/ALvsBoxDGGMZAk0B5oR8G4cGB7LXnKQl8dOCGBxHuKqRbP7wrBpVbPS0H0ZK1QXAB5+HUVhJ5VFFwMWVe01JLyuxLCWCNuUhWpCAIODAQEngpYSkKRDqNDGgG5PWAgC0kOf6apMagR//xJyu8CgiAEzVmK6/DvKWAVKt26nPRUjZv6O9JIty/zpnK49l4GnCuHBKRHJNnUMa7JEx6X9ge9FxGsqdoAQtn32WmSjV26fNqfgHgbONSYY6ZjP8Vi5wrWq6T5KuI53noHXe7BM0GLHZk0IFBRp7uUpwAGQVQ1A4WHgAHUJD3RK4BsgDHsOKRfBKmObuha3onUwHlSKKcpVWYYtAUIPTkLKtZhsFAWhNhgo9HwIdZdk+ygZH0u3MWxCsssTnt8ktwsMj5QctzBG5O3Is0YXdvuCckWjqNwrY6cCktHRZ7/SNQWmIQ+uSxokT10PDWefhLzEef1mRBk5GgI9HkVQoKKWHu0xDg3NVr52e4I6KGDRuN9ctSpOE6pZceaR+hBsftDEYLKtaDhGBgRWmS7OBjLlLztVlxIYdLbggMEQ6CWDgkaU43PZFGb5YIBhXkn5he7XhUaQLaB3kOgo07457JnMBKnPNje20XXJ6/PQaST31wcpDMOGP1sluDbX/921S0scc8boXFgAgDaKPtsPi/DSrOPRp+w7qugzvGKfMWQbEe1GS2cIAQhlBINgEOjMYWAJEeEowRjIFAYbg13M1vj4TPLBAMP5TLoTBqQniDBIJZ+G6bmlqxDMjNvM6JJW0iK99YAcqOuptXmcvXYVAhiZg8fYo12s0H9SVBWSC7VwdJxwWUrFMOs9dTm01BD7H5GCOtBlAkTXs2hM9M8qkIWuQdh9YnRpqBjjxNEL6PgfVfOC8JytqxVDsZ3UrqwyhKE5AUJbnFHIDAx2yl2WkFSJYAuHXiafXok5H+RSfsi18t4Cw8NKyr8QfdgLPopAJqeVrj8C2FqKOCuxNkFz+4NoqBSubZje25J9gefnbEaX5WMlqjW5y5Lqy47LMoyCzy17Rr5c2JFSzaYgK+0HFrHYDu7YFzWAUHF1Qj2sejpPkXAZ9q+8oC2bwufi72OV6wFQEAlOGRC0mM0AVCvixWN09eAUS4jPRXvCVI569InluXv4ewsM853IKxlHJZxc/OKlhZGOQFcj0itsCWy+dFVCmncoN5ZZvIJPikggwAxMNxZqdWPZHkC9z5IBiFL7fTBwFIsUBcCsRKvsLB4UBhvUB/sN1uYxJOPsKg6+MdPWvuiuNaVXvh5WhKK3gWrEBAidPdCTMIIDgLATnGQJB6AtCRBCNUmggPSf758BtN9fYJglVIlPyAAHhJ4JyaBgDxjwWT0/NOEMZLxA4M1PakWsHRkzmCPGBxoOPci5pKvCjFQYQNBgsPTcE6DYWmi0N3i0Y06JHhI4fRu0l8YjUPCcsX9mDNJBoS0zKEyAwPfpdwEId4j+EglSC1UQhDo0ZFJ6fvlzTw0fDjDckAydLQa/zf5D0ssp6v+BEYBnF3qbiJasD8H30r/PtzJWSCdzy2CQZQhzTr8nO/DnGp4I4DwoTAvkdhuED3oCwTlQSDkgvZ0vNzm8AsNLidsZsrEJzRdWecTzzBT4ExUG9DA8O8u7dpIkl6aViUNK10Z3P1q9F+Tl8CDo6klJ25VeDAwAEfEHE2OI2bskdaLMQDCBAg2RoRrwGJ0F8HsyhdHlickjMVXvlqfbHF6B4aWEDzmLwIuyXAcOMcOhbBJ53rl4/sdgkZ9n2nnh25eqJxFepgJAR/tOluYRkwW28JbYbB73n/EHTSFlBLpcAWoDEpP7sRsEZQAAnQd9Zgq7gIAUn7Bz3cFwJsZjLRna9+F4JfJDmCO6AHQ30wuYYC/JNfp80FMZrdzZSBRGrzm0eeeUi4x1COihSImQj70RFubbbJaFGzCXE8tZS0ohf6gEU7qqURjSnc/tGnkl0+854zdnDSIRaW6eTY0FeKN9BAhNVJ3PL+daAF11SCriljFMoJCMj7N9oWeAJrBI3xlQ6GmgeEG5DWDYu/BTN+IToNPXLGE36LKk25yRosOhZ0ueedBngSOMlU+QefA91JDp0Z27pdqm/fwEl48ZwQIX9s2AkMCAAV+K0sOnPUIS4kFiHIwOBt3jk2ZZZxBzzsw8mHuG7D4oNI832IDCJbbgHgfN/SaAKDVob/J8RrkNYPgQJfTINLvRKn4LtoLHSPK6ANO1AZeB4rklYirclcMy91ORF1F4BKndfC6jR71cGDAWqh76+pxxcTNDwElQmPfZUx8GlrDHFgBsajlSP3BQUDe4ypDTDU8Vf9qtvWlgiIvb6Wt9VWhu4P9U76D45+K+/1hxiSW2/Ee7y8M1gEvFPyXjcgf5+3oMmGIbpu9LmhSRZjjXmY0Kk+ZKb6tyLYSCtBiDX9Y+r98tHZ8H/kzdT6kJ84x+bt+c1/JQmdjCcL4wIqYVtNJyeHHL8qGWfo+tQvNoZ4gfJHXirKExchu2+2zWggg3pm5BJYHE0OhzXfIZJp/bBYbpwrfrAIzfD2mo1AsdJCIIiAM86PwO8gyRduzJJ/TvoQGpUyJRTV5LGLs0zUykz+k4NIJxn+gc4wWz6pC2ZmXlVD19WPuAdpAwQBzX0giDG2BgIeKASTDc4aq8NrKGOSR8Ag+Z7glDuce1JDowXBWhqlxGztlLBgYPoY5aDgzf5qMQ8evupwZgT6IZcxhAgb9JAzRqOPA1s4XsmpzdlZk9cPDv2R2cHWxiLNIj2ayh+cxyu8Dwvkumi7I7tt8vKaw7kHpnzkGgvEvnSagSrYMh8zmQB/4MAhNgQX1Bnh4OPYNcViMGtnCSOewwBdkHAYY4D0bHOM/EunhB6b+GAvR8ctGEISL/jIj8joj8TyLyHRH5j337p0Tkt0XkH/r/P5t+82si8l0R+QMR+SsPbtXzXuMnJvMip9EpHitPUWl2RK/NIdkJAArPQH49qTEPRM3Zg6Ha1SR6ONKaIlI1ViSL2h612Yt1JL34ywbA54E8DNzRA3ExloH9wLMkqUIMTCKfA9N/cbZQ5nZpvPYA5qFj6hrb5lsA/4qq/gsA/iKAL4rIXwbwqwC+raqfB/Bt/wwR+VkAXwbwcwC+CODXReQiFzf9qrtmNgOI7sqKsQ7iu5BaL7/YzI27cqSitg/OPqgwWm62J+PfU6zS2vqr1vNel71BfyUIaPGal1fIvFLY2dc5aW1bj4Pl/jJA1LTsfNVuOA3vCDbem7lW58kApj1bw46NIRjBRp3QpFL0/WLgvwO5+NTU5J/4xzt/KYAvAfiGb/8GgL/q778E4DdU9a2q/iGA7wL4wtUtuonwvkfIRt9OH4b3cj16C/XUZ5iRc9sek23qYJQBK3z8+eUd/9HAdS04BCuYVpCiRKn45pGRGkl1Ud6t5uM4SCh2yr/1/7NdYfYuYP4v0/8BMHQChL4tA8T2WVz4/Axy1eMTkUVE/gGAHwD4bVX9ewB+SlW/DwD+/yd9988A+Efp59/zbfMxvyoif19E/n794Q+fcg23KZFph9BPH6RKTKzhqe6nJ8k08FUQRU5H6vuMqsU52QGHrBrEquZkDg4OAQat9W0JMPr+0+lEenXmZIzUadBvMihPgIO9V8wMYrBhzHYGfh5AQ/tEmgA52Pf0eohcBQyqWlX1LwL4LIAviMjPn9l9r0dsWqWqX1fVX1DVX1j+1J+6/kjvo5y7I/H/PWJKUb/whh7QzBo0DfakWsikXiABxqnlEeeqTwHae+Awqxg7oBBMYQMKiS0UqhPJgvoOb/eDCJ+q/r8A/jbMdvAnIvJpAPD/P/Ddvgfgc+lnnwXwx2cPLBgX0hj0MnnaTeEVeizDEM/wXJKOJ5maVtjyaau9LytQuD0Vtw0KW0lp0/eXAOM5L0UKIKW7d5eSmMHEBPg8omqVjKwht22Khdhbp2GojvTSjIOiM2C0KOwzLIzc3LbV/PnF8wFK7d/tvQD0tSw4yTeJ9zR22j7S9419pAcwvcO54xqvxJ8XkT/j7/9ZAP8qgP8VwLcAfMV3+wqA3/T33wLwZRH5WER+BsDnAfzOpfMEMLDYZaZSma5eKT0bTjCs80Dj4LI8DiB88GBZ+ivbAHLHqhodx8AhgUJFN4oREKLzaO8EZ5bb42DVvF7mIwaULAuE1zF7G4DBvtALz0gARoSAZ9WC+0S15BKfje4WxMpN/p0u83Mq/fVcakq2MUzMAUD3ZNT+DIf/zZ5lWdFBfHjGHSiQFrgZV3B3cCBw8Dlw8Ov4fngFyDxiUDxArolj+DSAb7hnoQD4pqr+NyLydwB8U0R+GcAfAfglAFDV74jINwH8HqxMxq+ozuF3owSVAgCdqNjklrkY1ZX13dlo58u5988yLMF+UVJJ+bPSJnAQdoYODODsU7WDgV+/ZD13jzFMAUPD9vhuutZz1zRLWmptGJSfpDzn+efAqwag2LNQr3EJEbM9iA10iK2TaXeUq24Pa3IDAhQomkhkc8bkBgcGgQdTIQa6wj4rYKyBEZssyCPGHEKtUGwZ9DOziYvAoKr/M4C/tLP9/wbwiyd+8zUAX3tIQ7JxRFs33HXdzRBBL03ygu3gzbMgVxyKzlF8gJ25s6cyD3lKHjtoKQBXIfToMxFnDmcGeRYiGKikjErOYMkWIfMaEhH1N7U9r3d5tuET0Elaw5GxClmNWDrYRn2I9HMLEELeYMLKQy2H4eTByesABgC45EZ9ogy2AxZ40QQQAGKtiWYXa0sPw8FAUXQChwMiMpNAQKOh8nh8PD7wozI10N/r+N4qelEVm+7LC6gZtxH5KOhVhxUbY81gqX3U8Y2myto7nwDn/fcXDznpyRFeDA+iKaEiFPb9rDoQHBhUA8DqBOjosT0HWBygBUNl40dcjA94p+1clHfwRLBTdluCCqYFgMaCIbyQuFesWQlYzUr12ZhAVltfMVp1u7jQcxto0/FEFVpKB4XaLNfEV9DGCuAAB2+71gCH1f6LM4UCoJbEEMTxs6AzhDhxAgJvkjSfBGFgoLk4Lbc5k3gpuQ1gAKALZ0qBViuCWZLl9/HHzWXXnR6SZj+lo52YsSxOvzOCTj+7TgqgGxYJFsEM9tt01gj5HDRbkvoAtxHM4JAqGL24MJgLCLvNi5/SQQGVbMHPSRZXY2xH9bgSg9QYR4MBZqmKxuKzBIdmE+DACpq4+uKT4I6aIE06oyZB3FMnnlFuAxicapmo+2PndNYTtFn6bDZQw0Wgq4PCbLgiTff4AkMe/22oHVt7wm5FZg6evB+NV6VBqpcspe0g2RB6tF+6D0mtGK5pzsL06wpwIf1fSixEY0lWiigguyz23bJYhWlmnbpNIWb2ZKTdeCP8e11cFx51iYHt6JRWKa5/BFMAxhW985J7Q4ARy+P1cxEo6M0Y1CwOVBoVRcal/E7JUuy5RRvZBq+O49dTAHCtzAaYXUDUvV7mqWjFJwKf3MiAmZwVbAJpG+dGf57SvCjt3EelN2tPNmnYj5DbAAZoYgwYU1Vzv9tByDEIiCm14w2MDEsAw1LxCSRCnjr7utsLTSBNbDVmNtzBIPTzYA0JlFhS/tonOxgcp2vjtq5vGAjU1jMrgS0gDJ6BdI9L3wb4fX1MD6QaAgC0rfj77N3ZXCdlAo05fsFm5kc8R/dWxBJ48EIvlNr7KNfKLMry7gQAMz5KtVtOJhBqkeNLjPNmqgHrUzoNGVO+qWaAHefl5TaAQYC+UpB4XLh0JlH2QeEqKcUgHEiDxWf5eZHbh0g2SObBSTZQzahgi6Sif6fsP+rA0A+j0+fYLlNHySJ9Vn8SpM2DfwaHcE2iPxs3ioU0AQuvD4ZJBkTtXV9mgfl9zrHYC3l+DtmEUcN1BAGwBw6uPKiiodhaFQfj/xJal2VoSpMU7yADEAQzJGtQIBsiocYaxIEgWinoxWcgeGg040PkNoABsIVHAbt5Sw4nxhAeek3vz1Q7/i8FWB83dAZD4/bLrnJwsEszOljFVmZmMxTbWZGqQPHzzLMhfyeyBYcy1heYf/ewi/TYjOyNYMJW3EfEzKhFekePQ3BQdZ2a73mKQWuiSjEbIoHtKt47wKDlcc/TfjyBTXouNm6NYQU4qDgINFcj7L9qQzuIdQGqC1WCoEX8QvRJxD0Lm8IMEun+BFuQzj72ZFiF7Bnw4jaAgcwAAOBx3RE26h0xqQUXj8UZbRItU4FV2GBUPsWTx5wOdqKIiTRvO2PwUcNHHZ6HZD/gZwBR/ryfAx00shEO3Sg3UPlix8j2gYtCL8R8bSmoKBbQmStEf5KSDZMPlT3m4cZN5T1XS76CSjAGU32aB8eVzpYWN0jSvhAeNYUs0m1JCVyDAQKjrWE2RHJb0V1QYGzDc9gUZrkNYAAgS59eIsMsdNn0P7HNc5Ir7UQXyoOmwWbIdb2+kdnQmAffDBzNplJRCTbQv4N1GGDTSQdmotIBJBvR0vVZm8TppffKh1jvIzpx2QBJruc4VxLaXfUZQBQ64eesIcT38GIq0u0LvuNQBHcDbM+kWswsAQgjsFXgVmMr/nyDMYiAq47HSldBZwtEGkRKsi2YKqER4oxBhRgYgPZuHZWjeIvsxiBcv+q2hqhW9QTWdEZuAxhEIaFKmD95zlKLrLKrj4l4uFqKB+40Yw2+VuST1j2YY/sp7LTup+JiKIPs8EELKdahmlwMGJT9mbp4SbI9b8kDRVzd0oVxDGyTe2eKxJqcFnfinXOmsJMGwMsV7+0KO44ZQJ2a+4DjewDhWRkP/oxCF6j2fgfA4heWZOU1AAAgAElEQVToqm0Awsagpu6ql9NT6ewBDYWBcqsxU3rEJdQEV7OySsF7Q4Cgd8nVQxojpcDjPzTsyGGzeBlcuBFgAFAYHQfYA1iccic7w5PiGbJn4oTO+qwSbsad73YsjAJ7+MPv82Bv2Jae1J0CI9fIHAq+p3ZM3ocPWjb3T6bn5wwiDH/oCwrX7kaQorZ2Zkqw0pKjQe2ZBltIrCHsEINBEolKzG2W3X70XHITwCACLIfeU9vBZq4h2zJWDCbXSvdluj+J4dmDWXwgMMKuaafdL93p99xuM8f2/cYiqbmasXVMrbCYfvqz6N4MI2YbaxHsiQOkeuKUuMHR1IluX2CS00kwfq6AI/dEsLgtkbSzCJcBvGQTlxARk+9Iwq0pPpqLAUKpnTVI9QlPBM0Dm3L4tNkmvP00VgJxT+xE3RahsHgJ2q0ijDsahWdjEDcCDIrDwTuEAmVR1GKxDQEIYjeh+78Tg+D9ySbv0itEG2qbeiKL5UnIWhGZfWcyqYJlXMiX2JUThq4Ih8PU8U+BFA1irucCyQ2qPTtz3l9Z5ozXshTXWd24RkAgKFD1yu3Yi3bU/gxC3Xmgh0ADpHks7cZTP8lgpJ3VtYtG6AewwuSaDTWC2/Nnnpvn98V0zZagkNVYgxRLuIr4Bp/ye7M7OARTcLWBKgINkWFrUGxiG/aeS2x/IkDcCDAAd4sNzqaC41JRD8tQo6FXw3Ff+QQGYektGFZ7Mp3MI/UauqFu7vyzMBJQpo6SG31mIJuV+wm6f2IQEUEYKyxJj/tI5cnACkaXVIuwHZR+nUM6NTxdOu0f7YLruhKfIbguZDmD4R5m/v/tfUusbM111reqanf3Pef/ndjkIRNb2Ehm4CAEkRUhBaHwEAlJlGQS5EEkDyx5EokgBsFWJCQGkQIDlBEDKyBZCsFYgihWBoBxiBhB4pAHdhwTh1jB2MR5kTj+7+neu2oxWGtVrdq9u0+fx723708vqc/p3r1779p7V331rWeRD19foIFezIh59FzHhdXH2Nka5sblI8epi+lGsU1AvV4ULf26RUQGMEpsywHUucFUinpN+n1s/duyMVkBA2Cnfljuip8YrIG33oKDch7AAEZSr0RhIKWIyewMdUlxNGMkq0/fAnL9jXA3vjIGo6nq7aCoNPC2xCPfSUwOeSLmwne0DBWHcv48QJ3dqypBVFWNee3CdixrR0+5hUGpgc9ckKZOVNZwgD0cvNbTL7OL4Zhv1yQra2d3Co/J9ia4LfOQaH+uY+2ATSbHL6LeB9MBXPslLZuFNWRlDgSEsRkJLCICREI0IBGSXBoeVOag7GHPEOluhri94djbLODpgYbJ8wAGYqyTuA0ZwG5KoFhQIrcQdRvsZsUF4NYN2u+cqnrUvAuCGIbYKKAe6MQqxve6rtlgrzOyp6OHOq8f3EZYVA2xTFQCYFWHfH3DZm+YD74AhOLCc1tQU3NNLkQ4Hry2h4kANqqbTq7VUUAvs7T87ttTVIvHlIpB2qbAQCZQENYQclFPmGbWUgMHMkZL0MxbAYouRduHT5tGNFcpnJfC9q0Rlo9glDwTYGiqBDNhiFncl9XOQK0Gv96sqrkdpKTsOjfLuoZleTCaHaKtWL0cwHSw8d3JD9gVZOf9TnyKHiyZOo3a1s9c07xtvYTuuDO2IE0QcADQqUvVTakVlrxtoXY+84zco9/tGdH1Ae7lWxhARcB7dDoG4dO36/3cP8aiHLI9WB9YbPv+9s4wmiEjuwhzYQoIJM+nkN5y67SkapgWcmGLjAQ6tluZA1NLGENTKezZ1LgHtG1dBNU95TyAAYx1VMbAhBQHhFCQq/GRYRV4683Ttzaga9dwoED6GzLDpSs3RpPVaHB5FAGwrMqWdNVo9SExQ9peCbZ5AFPNCMThDnpMzK9ug7lQzxbUzgB2hWjsv/fIGPAF80iEBph2bA+g3giYucU4zD53lae6Czc24piGDWZHEAgn3hP9bc0RYW6h1HaNPlNz/lvP2nRbjaT01avsPtg84aJX9wKlMoFIVD7S6s1VpYBLgA0kKdlkiVbyWzkwWsyHe1WVwTdZvRQI3GEAwQH5yw4MgRir2DwDq5iRUsEYHWOoAU9yl6o6wdgfZG6mI0KLUjPWUKj58h8Sb++ldvQjT8Ozhdv04EPHcZGUPoahM6D5FZnm4q93XnPB1AgLaMI+o/dya6Ha28TumUuysvtBs7iNlsKt542AeTW6iMn5NT4L8aBrUZNqL8BE1Q0pWmqQR2YTmk5UdstDBgqxMpamUhzMvjSVot0YvSnu8yNc/tkAw1XaARCvxDpNiLGoOoEGCqQuHGUANYNv1j856KRoseQEocgswEJuIDxz3bSrManMwhZl9R2Y+YjermyHWY6RZwBjdoX5mgqF+0Hir9WqQftSbpFq+TZR2Q7cm4Ke/j9EzKoOtHBpa+ttwKMGSwANwe7DxO4jvm6EeYSIAE2go0naEjR00edSCABo1qWqEjXpzLOGLthJ3pBqiJZ/w1pQ1P/2MeQsgIHAWAVRJQoHDCEjhSLxDGaAJFSQMEDwQSHV5ePHmqkTRsuIm+vyAFVujXKDZiH8+daAmvt0ziO/qWBiUXjGPphni6wUUSPMvrAHDqZGUM2RqPaFuRrR4o2OS0GXI3H0EkPr4HvfqZoHoOUrQJ+3MQQIhZ4vU9gyWA8whz1DbA/Kt4qym06N0HteC6oUEkDw52eqJh1AgvaCud2NIRSI+lEOsAZu6kW1LziVQo7VrqHmUzzACHkWwBCI8SSOAAQYNnHCkLLYGQwQZsVhgZYpeVA/JTSjokZAcnEuTKABwJLMwWMOJMfYxpKlfKZK9Fl0c9rT68C1Se43nQrh7QyHxIHCHmuqoABVMdAMgzMde/GaTgGQ2o5Z3YYlccbgarh0925PfVg4R9fGU/axSWBuX1g6llfhStHJSYHMg4NOIKQ2iBCDslZW9mD5LmjsIaCxYEbnhbMvOMxUCrT9gTZZ3lfOAxjggYGwihOGmBFi0VgGbi40vWkEVQvYPrTjmaGyAkeUHZh90BJOr9FwX5XDD1igGQDnxjy//7H3+ts+994BgqktOcuMmzW605T1xfJtoQ6Cfm0IY0Y2a7sZab7YK/ju98cbJBeueV5xWglfE++Z0M/2O4szPtkGsjABeFDgYOHZxhR6UKhqRdHJKXQF76R5YnkE6/oTksyHrl6DGG+p5U8YS1gitKytqcxB41oCPwgQTM4DGIhxFcTGkBGwiSOGUCTOPHLNl2jr9qFSLu++nEszVOoMqDpZVSWehxxzXy4xChMPKF58aXhTKQwEmMEW+XhMSNUInb2qm9ItoFtrKT6HIqxdu6r09SS9z77CwVJA1POwL3gwNjUO2geDhp1PaO5UtT7SRKBYQDFIWnZ2rMH6c3Bg4W0P6FlDVaNn7svHkrMABgJj0PJrAzLWIWOIGSllUCzgJKyhJElGqRMJQ274CZ2hhqCaq01nS46akh1jUzmOqRfnJIcKnHprv9Uci1GuKzoXpUuYYu+deAku/ezkEMODRin6Ze+UNZidQTKLXUQkWug0uUkQMLuDo8jPABSAswEGYK2KWQFhHaZqgKTIEgGZgJKAMojC6fsul/7mAehyJswGwRHgTM2FWSxUWv+bx6MbJLfYER4iB12SC2zBPi+BwSFmYRml5oGwZCk1NHKKXVBTS293FMz+n3qpS6ByF/vD60XMBuFWt2rgwLWgixkiYeHSgNgfiFpaD0tKgDdEPmtidB7AQIwhNItNChmrkJFiQdTQ6GIJVUnAA8yyILC5bJZi3UntEIUbsBLc4C/Nlx9jT9GPgYK5HO8rNvC9Z2MOBt5m4GWe5bm0H6nepIyBYuhBIfiCLC7FunomnF4b6E7qxGKU4HwpvdeTqOdhUS3UKkuicuhyAhNp2f4W2yBC9TBSlEuTr6gFR+lu1f7wLLHhLIAhgLFRxpCVMewZIBODE6EksTNxITBz5wfuxG6gc2vZbNjqNYgawUnXW5i79oC7GdXmgS9zKdzvdx/JGUvrUrJ9tvZbCrO5JZ36wDEASYCCU2iZlF7V8sdW9YJwYIATLf6u+/1C1rpFSp5qz6jFdPV3nevQAfVeZOKzkLlH58gkIsyU62xPRRbFLSSJVZLsp3kVoD4BWDW7ov25RkQ+YzkLYCAwBgWGgICBhDEMoSClgl0SYCiD2BmqC0dViNqpD59A/ysvixoOnYsYImvx00e443xk8D9WZz0W2WiizKKuRelBwRhCMvuC3I9W9wKdu/Aiy2KehcM7qDrBGqJeirotAVBAGAsKQsvCJFR1VirHaQYm9d6L52FfPRtg2ARxV2YO2IRR7AwxI4YCSgUlqTqR9H5nZQ2ZGzgckD6pSj8rY0AQlsAp7q9mtPT+MeWQ8XBhvQt2DIFI2NJJ4tWHqjYFDTM3+wI5/32zNdAhFe0it0sNgmIARXMp1JUZGJhYK0xb0hWqrsvECMGYg9obvNfiGasRwLkAAwGDcv6AgnUYsY5qgIwZITBKUNaQJOqt6NJme4veKvAyFmIcbCY0VI4kcQwxtug1ABZ8ZO+rW/A+4n83d6kdc1cufa6bXaebUdg9N2xHd0WFkGzVWN2TbCHQpg64Y/YVs1yCGvdgXNUIQqdSVI/REoEi6uwPXc0Ff9ssUvLUZzBnbYdcv0d+S0AtiLOnmvj+4O+XbienHvWeidJHR+pNN+ZgDqRQk//k0NkiUL3hUT0THVt2N+2hsQxnAQxiY1DGQISBMpKpE7EgpowpJWELiVEyVVCQTt0/9BYp1+wM8zLlEhGp9SALiWVzQn3gNSJxtiz9ncXbAsxwaNu8IXFuRDT6efC4LLpUcSHOXuYBO7GBApQpiCqh31U3bvs9g/dZQ0BbA8PO4QOR/DHgAPqAdGnXHjhtkM2DxG6R7lzepnPq8zODIRw4WBi6tdEMjkugr+ery+RZ+zXkmSY91lQkqY8tK84SIKAuTDmmrJrdVrayOKva5e2W+cnxEeQsgMHbGCICNjRWA+QqZkmoShLPUCKBXJHYVsTFU4NeODr9LBilA2o5sxCAyCAO7UHaIH0IW7hNuk58CyjUTj5Xd+zzbMk8AJZCzC4PQuwMDhQs/Dm4Ai3KIE5Og77IoogNS/tU1rBpXYNCigZBg+2AQAGcxQBp2cSkKoWlBJAHhGcsZwMMq+o+yBgoYx0miYCM4rYMiZETgwcW+4KlYpu1ts5gB8AhAGBnazCVwlKxWYvF2mDTGWFp9ap7yywcetEdCSwzhXnRFe8OA/bVCl+uzdsVlDF0TGFudAR6Fczk8O09bvw99L0dq17DLce4izxPQFvyxlj4uoED0MChAMQt2tE0BYld0IhI7c8cILU+MyuzfT42hpNj3IgoEtEvEdHP6Oc3EdHHiOg39P8b3b4fIKLPEtFniOjbbm+EqBIbGjHQhE0Y1QCZsY6T2BliBlIBR7SYBn0tUqiZrlu3BQUVopZiHFrKcaPaYfmBH5PCGp6sIcpzI2IpD1tIF6izj4RAl30m4dvsEoJ8afhWnt/ZGOy+WEEcf0oLlV4S56ZcLAWnz2Hp1WgwHXR1Am6WtHiArFWuSxE1Rz+TZZjO3cXPAiR8IJx9novZKyxsWvMqaNLXmNv/zAi5IIwFYZQy9LKNETLawjWqSjxruUvw6w8C+LT7/H4AH2fmdwD4uH4GEb0TwLsBfCOAbwfwz4joaO11IlElBpoqQKzVM7EKGauUEa04rLou2aVjL+lV9TtfoizUEzr7BKoLz8KC62Kuuu+iWL0D/7qLgevAq5Z8L9zARfMg9jp8tTPMzuuqWlsuhLEFNjD0oGDfzS6VD9zbg0LtN/6FpdfrRA5WAvcGUPZZsKXmV1DOAgpjRhgzaCx1KQCaIICQ0ZKtDByAZw4OJwEDEb0FwHcC+HG3+XsAfEjffwjA97rtH2bmLTP/FoDPAvjmo8cHsKEJG5owUFaAmLAOI1ZxwlrzJsJgeROOMdgAP3Il1djo9OdaAdkNFJgLz2bZ21jDfGCfKKw2hKWX7FD2VYcKPsIWOGvdBds2N4TVRWjd9dAMDFIDiPYd6j5NPbujmPvTXq93WfIuGQi492Tl/XMGpvaqDGJSxjC1tUKMOcDqNjwnO8OpNoYfA/BDAF51276emb8IAMz8RSL6Ot3+DQD+i9vv87qtEyJ6H4D3AcCbvyFiUCiMTFgpOBhjGKK8YsooQwRPJEuPuzUnAOxHps5uYLWA20wWoC4p1IOQrZ+oIana2IUBcgZGuVLUluAAzK+BUT0S1AKZPEPQQduB6v8H4/i5yjwehmShXN9/GJAo3Bp451a0ii49m/mOFO7+citjIKLvAvAlZv7FE495zMzUNjB/kJnfxczv+uo39c0YlDmsw4QnUWIa1sOEYcggZQ0SBcmSWJWAMuj/ud586Aqpty9Uq7xagSt7SLGLFuyqOs0qHz2aUGgvG/i1irOsHEUpgYZU06dtm8UqQEO97b1Fe97aTobGDZwB8Kl0Y6G6Vc12AtRiKoeu7TGfzSni2zd/3fbTLvYCLSPz0Mu7Lx2bkM9zne50OYUxfAuA7yai7wCwAfAGIvoJAL9DRG9WtvBmAF/S/T8P4K3u928B8IVjJyAA0ZWeieAOHDZxxCZNuEkZ45AxDkFo8EA161JUC40INCMNoa1ONTfqq65LpvOa+xKh/ca8EylKZ6wqgytdzt4boP/r2ph3M7MbQ9kDs0ASr2Bhyj4HN0h5Ng8KXAHEeSKq1wGHDYlAB+Fi7HoBAGH3mozl6eO02IF5kRa4fm/xKjZCrD88bzkCUnv3f7avrGZFnaHxoPrA7XZZ0FONkHxA829lDMz8AWZ+CzO/DWJU/Flm/n4AHwXwHt3tPQB+Wt9/FMC7iWhNRG8H8A4AP397QxgBXAFiRVk9EyM2cWqsYTUhrDLYGMNgKdlSq6FEcqtW9baHisbujlW/ffXp9/YGTs3d1zEGb+mfd4Iw+/4EodmsIpWEJY+DgmRI2lJydb3JlGR7SsIMPLuJmgehWZWeFdl1dzKbcdo9Q529nrmE9vIBVA9abdvu2bFZ/JQZ/TaAvAszmO97AKjN2EiVPaAWcIG9537/x5KHxDH8KICPENF7Afw2gO8DAGb+FBF9BMCvQWIJf4CZ7+SjG6oRMuNJHPEkjrhKO2yHhN0UMaakoKAqhOVQJJvQbY7RWcYXtQBqNpvo3xCXV6AWIyQZLDUgBdGtMK0GP5aL7ZnIvPOc2KFp1pnmnyv7CEFccl5MlTDVwUChAlpvYFz0CswBYQEcRL896XLuLbVm4rEw9GpLWS7t9mB2sFTk99gxzYvlhR+Qll9cX1WbQrv/VJ+VVxnq2pZAZQ0PLeByJ2Bg5p8D8HP6/vcB/I0D+/0IgB+5b6MiGJEkZ2ITRjyJO2ziCk/SiJshYbuaMK0ieJSYf05QtgBAA0bkhvJiqjwAmAHSCrhUcNDfSgfR5d9Y6uqgzGhqKb2a0qkbDxBjG/YeaOHT83oMHhRSYwcte9J5HSKdNPt2cQMvWgzkgwZOF0aFg6XSbkFKwtV4uRd0DUeriN9q5xEbD2WAUgOE+auuiF0nNNQ8DcnNuH/7zyLy0UsAI1DBQBNW6rJ8Ekdcpx0mDtjmhJs0YDsU5GqEhNgcEoELSxSZjs9as3/pZGTAquAATdaxNShcSJ4xBs56LIuXh7NhPGYnnMdRLNFNBZAuldqDggJD1WuVLZxqhzpITQ/N5ktyW+q2teXYbqQh2qecU9u2xz6epxfpFFXi2Nd+bpkZGsUwPLNJFPNs7B/nvnIWwBDAWOm0m0GIzBiQNdhpwlXYYZsSJg64GQa8thpwsxuQh6gDgCpjII0p5whJyza3Y+VegM8UBPSm6sOSajm6UEiW1snvWCLsiMGTLi5iy6D50GmvBtRKTf0sf7BbzNnAXP/0xzb920AhxVpfoUuQCmovoZYLYTq3JZstMYR6u+YFUDw7clGPdiMJOC1Vm9AVewWhLeFHknXJmqtR7YhECtqsPv32XA3EbWVwW9eTTf06JZ7C31sLTFJgYaJ9Nc7v+xCx6zKVwBhDoZmXob1sQRoKDegJOhkasDwvVeJ5SFQDZKSiXokRV3GLkSOmErFNCV9OawzDhJ3aGtjbGLKwBogGcPTm9Jl90ExAgKGl5mHzjKoVRAIKUb0X1gmrFd1UkUeYneZGM91WLdq2PTam0IGCFV6Jji4ZKMB9XjI2mnh99zZhaB7KHXqj3/W2c5w66S8N1EdwVy6ylccABTvOIalRk9S5J+u98CtUHTHN3FXODhgAIBC7CMgdxhAxhoSSCCMH/PGwwVeGFW6sgEuNhhQvRImEWLjS5sWQaavnQG3gUHCAYHhRoGqGsIMKCubK3FMpHmH2kJvQBj81O0Hdx4DDqQ8dU0i9FwKExbDnRdFreF6ZfEfFmE0BUBkeaVnL3t4AoDdIqu3o1sVpnkGbjw72W1cxA2x1KnKA4GMVani0xkpZOnbty+XEZ31AzhIYIhgrVSWuww4FAUVv5sgRf5h22KQJacgYUwEPoborKQFR/bhzxtAtNw4b8E5K00/ro0uQwhrB6KoZJGU2Jm/dvO+sMndpHgIFDwj2nS+64uordAVY9Ji9C9Cdj/eBYA8Unse4OsIgahq4funBAUBvfDRwuA8Y3IddPAIj2RNlAZTVw+A8FPuGSMcaZurFfeUsgSFAGMMmjBgRkV2PGTni1bTFH6URQ8rYKmvw+RO+upMtOgOgIupSCHCt58BaVdpT7gRxvJK0TtSN0kDBipl4I5fq4ABO1LkXGAG5aMU5IKheuqc+KFMoPrDJqTlOLe9PX41d8xGJFgn5LAOFyNkr9DnRY7ggA8xwdDtQUGNYHcM49rtHAIXeTtNAmq1qkxVqYYatVFXVCq0zUlnDI2HUWQADARjcBWUwBhSsWFgDgrCIoD3k99M1nqQR62HC0yTl5Tmyzp5o0X1mmFpibh0gGLvQO1wHOLU6B545OKbQrYEJ1MIc5FdDPlVm6kJnTzC1wQCHaN/Q6EEhhXbd3jqlV7bYgQq3GQlucDxvlYJQbT4AtB6B8AT5Wp5Pt4ydb+M8xiECbFWzn5ccMnYuqRlzb0KWOpAGylW8oVg/1//zeB1+GEacBTDMxQyQA2VcYyefgxgkR054JW6xSaNTJzTYKTHCqCpFcOrEgnSAAMBclzCGYTc6hTaL2orLNlANIGy9sMdyWYbmZrQqTB0omF2B9tUHYwqSeaqzn7P2+8/HpOuQ1ueOAKw//qOJP1xoG+pMarMqIM9mIcbhheR8HErFVjnpPjnvwl5ehDIIAJUxVPYA7Y4PvOyzBAYANTw6U8GAqaoT12GLV+IW13GHdZJVsbdDRhkCyhBRJqCFj2o1nElBohZ1ob7TkemwqLNV5/OvgU+oMezileDGGoKCgxkitTw9gNuNTbaP2hpqubm5gdHZFYxR+CXrayFXc03OGcMhIZLZOKpKFGkvOIYWZkCpZeG2Ez1Yt62HMr3aPnu3qWYaMrvtVlcxi6eIajDLCSrEixbP/gzkU6jslKPmA6nNrHu2h4gJPwwbzhYYTCIYRdkDINWkB826fJJGKeIyZJRVRFkHVefJxZkDZSLkAe1OGRAYMTD2YPwrWGfSUnCmxwWAWQYhaewCoqTRcrEgKP1tLu10h8rEmyzZE+bqwxIoRGETh5Kijq55oNIYlSBjdW8qUs51bu/lANAZcBcrON1XZr3aqzVSvESzDqcCqrMoyxKEWruCMutzWTjgi5aqumLRTiRVtqS8QF4HlIGQB6ppAAcrmGm/r6tl31POGhiCwl5Wn6FlXVoRl00csU4TVquMaVVQ1gVUVDerRhpCmRiUCWG2KpVpDAYQZJzMGXF8sE2z+opbU6r8iiFSIquk1FiNtssCEnPj0tJK211KtGcPFQiCsy0YiLjfh35Q7hlQl8RdYzXKUj/4awWmChT225kqBrfvgrTfHW9Saz9mY9kAClqvQNggpahrQWphEw1EE9WO1XMhCsY8QImf1cLFxRlqgH3VwoGCBTdx0pJ7ST1sgwLDipBXHhSopgBYoaI9BFDwfOkDnBjLhnthC3J1K2TsSLIuLUx6E0dcDzs8HRJ2q4hxHVAKAYWQtXgmZSBPBJoYJaPvbAdsa5VFRFajlyRdCcVvsQ5gGbg1XFrjG5gh5wawVH+wK3dfbRZh3+tg9gTtPEbVKyhUkKDW7gWpCVB+0MPZWdQeUYGhujaViVv+GDUAmg/0pYG/2J5TO+seY2jbQyatciT/Q4auCSmAIHgY2u8OpL8vRjI+C7GivQbw3sDs2GIFhZUwBAEIAYayElCwuiPCFnjvHh9LhruLnAUwAIc9UoEYKy4CCgzcoGClBWNNndgME3brCSVH5EIoBaASEDJQMkATwCNQMlXjDYB248zaW5kDUK3iwS0gwqp/2++C/SmC0FFtDxqXyiFo2Kqdj5u+69c8IGorUHcgQG12WQIFsjbcIkc6SA0MUzVCAsQcAJhP3MDAfQaWAKJXNarM97+j+FqHYWKEiZrtKBNCYIQRKPo8grLGg3kyL1q8yzk2gBdbmYKBgkNeoTGGCgr6otZ3zTvhQfS+cjbAcJtEVZ5WlGux2CdxxCaNuBpG7KaEaRVRMoEzoWRG1pkkaDEXmrSDWee1PHfAgQJVW0MFCEuSCoAENsn+1UuhRkDKGjZtcfZZAcIekGUCss4gzijmVYV9xoB6DpvRvcFxUWXwAHhIPCuIVCtglaTbra6F7edZxZwhHASK5f1ulQNNpwLwSCgTEEdhcTxxzQORRxLAXMTeYBW5zkx6t7O4mosWIBJmYOCAzrZQK5Ylrmpc1Xw9INhkd085S2AwD0QAI4OqrQFotRquwg6vxC1eTVvcpAHjKmLKokrsFBx41Bs6CQ0LU1sdm9RICdVZ68zC0MGpXnNyXzCppRvNfZYCoAYwJnVtaoIPkwMIoILBHCj2AMFUAysc43X+GTFcB4kAABhaSURBVCjcdcDN3XesBs1a5EZzTooLMW8qhtdr/TGWTjTb7ljGg6QAIQFhhN4bs6WosdgGRdGZeHLxDHfJobjNk3FnT8fCuYwF1hKDqEbGvBKmUFbUlS5kzxgUGIihCX/WNrSiLveU8wQGJkRiZCYMKGJnIAOKjE3YYUMjrsIOb0hPsV1FTBwwlYDMhJwD8hTEtpCBMjaXjxllbNKuFlw1ds1igZp+HdTa7QfnpO6KJEFNbel5c6WVBhAAamiaPrCqYgBdyfoOEFRtAGzm7kGhAwcXAk28MGC9acPVxLQBbx0vq4HLokhNffD7LjIFN0UdZBQPnL2pALzzzMW4tDxUUjtwicYWSA25eT+qc15gxcsxtnXXmhtEcrxk7mjHDtWzVZKqDQM1W4KCA6fGFCRhUBdyjvqQtU8TtUlv33h7NzlLYJhLcFcYoSnZmnX5WlnhlbTDVCKmIuAwTRE3YxDGkGKlYWHUmUVvoCWeVBDw5CC0jmbbxTXnXJixzVR2nC74BgoYFbl1RrOITP/g5ioD0A30FlnpAAHYn5m92PGPzdQ64Iv6y4221lqaxhgMPKKU0usYw9I5rF0u3f1O9oWF4wGiDrYoVf8lO0+U2CFuWwX9HKQWIXaBaSWhMoa81mdg6oNbnlGWXjSrsD8oGmu4Jzi8FMAQO1WiyEpVRRjDq/EGozrTCwiZA7ZjwjgkTIMuaed05wAbzKg3kH0Silcp4BgDsB/f0CBFPRdqGWfS3Ak09UOPKbjQDJB1Zl8CBNND0YCgW0DHMYUqbsC0OgYHbiw5JmD0NEEs4Kl95sAdfS2J+4G7NOiJe/WBIMzpISoFMZAJfGOuWn+t6r5Ut3RJJBPB8wyDvovYRGA2htBYg01kee1jFqQfiz2I6zZhI/1l1kjJByx6dhbAUEDYzThmdFO5vY86slbIuA5So6FEAQNAVJBdjnhtPWA7JuQxgHfiAqrUmB04KKrOLdfV2s+tPgMI0AUGK3X14GCRemzbk65O7Chd3ceChtpXVT1ZUhdau6gNQgcGXbAW0KiInae0/eaDstNvE/Xl+IfZDGUrgHlgWAIEuO8MFO3cJ05hdGBfzkFMM1bkkF0wm67cVCYgaHh4DPvPt8ptgWePJb5Wh1NfOIYGzGrXyYNzSw5AWbNT4dixN9aMX43VyM2F7m0Mr0vGENX4aO8DhD1sdCm7q7BFRkBGQKSCiIKpRHxlXOPpasBul5BTaT7hBCvsI+DA3OwL1nFnN9IGqtgZbKYnIKoK0UhDG6DFBqoWnbWdLIXWxKIkDwDCUiThXk0FO4a9t80V0A7PmD6wSZgAWodcWck87Zi6NCAiA4nbgLfzzT6LztsAxAZ6OLWnEi82veSACUlSBEpooe8F4oGKjd1Ue1C9YO7v1bNiE1ZcxT7OvUZ+DuzclTp5+WcwNMYGMpagfTFAbVgL+Px6YAzHxAABMJWesVaXZXY3fKCMQAXbkvBHqw3+ZFzh6bBCHpKUmh+43nxA+4fO/lwHoTc4yMvTcSq6f0AFB9KVt6EpsdV3xND4faf/mWphkhto1JwDBwhzZmDtmHdosYfs37vKUA5m+jVVojj91upollVbKxQBwFBAqSAMZb8jhn72JQWGoIBh/+lEYJgDSNTj7ybpshOSxKUUEpVsItAAlFGvJaDlktRcF299PQ6aD5bOgDQ7Tw1tt2etXiFzVTqAzmsFYZ24OLrjBp3YnEvWF3F5XXgl5l1txwGR2joTUa89EFBQcCXRLECQoJYIYQyvpTVeHbb4o2HEl4cJu1VC2QWhaoMcW0JglD0UIChjqNmIzG18e6AwFyU3tBDAUFBQ4EKh6vWsAKH7H7IUtxTrds69suSVWSwd4MBx3fH2df6ZbUGNjqyzFa88SygIqSCmgjTkboAbCPRNFSXLBnQMUi8zEJ8EDoG6ipAI+tubKevET8ijuCN5EuodMnQ9Tq4hw52h9nnKKef0jM1cxsmB88DgofTPzUDC3s8rNSkoLK07cRc5C2CYOOB383W3bUMjAhXNjygYuNSCsTsOGNWuEMG1ZkNGwKvxBtdxh+u0w9VqxG6VsF1F5HWoszuPaLHkjik06iUGrMo6q7FQv7ZEC31m9nDr7w0QjObboM/uoXpmAfc7YI8l3NrJ9Px+f/ZgV9WF/n0egGlNyGv1mesMlVcMXjM4FWBgUCwIiRGCgMKQcjej08JgDySgUMEAbXCfIjTbN5AsSFSYkGLBGKRKOA+MMhJC4mpgNsOp5RyESOIqzLOSXQ/JlTgWTl0Y7KdrjWzleZWtJHaQMlBzA3uXsNoT5BjOVuM/21vPEoqAZJj45QaGHRL+9/TG+jlCPA+D2hI2NGKgjBUXDFQwckAGuUCoovsGrMOI67TFVdrhyTDi6WrAtInIIyGXWG96mHSQuDwByq1QiXkjxK1pYaZKhy160gVBUfVXHrjIpcKq3k6n+QwSQek6gXpCllQIoGca9r+rMREarbYVukDqpVkBeU2YnogFPG+4MYWhAIOwBIqMGAtiLBhSlozWmeow/2xNSrrdBroN8MVb5DhC1IHFOh025lGQYkaI0i7Rv1uRHgOEPEgQVBlIMm9XEWEe3PUAYOjyLGYTB+lMLtu4LgDEQ6w5EZZBWZOlfDBZDSirM9PhhjiVl1SVDVn78jw36A5yFsAwcsQXxq+uny2ycUM7bMKI67BtAKEhXiNHZGfFGZCRacJ12OIqCGO4Hna4WSdMOeDpFFBYDT1JWEOJQNy1B0LZlc0KkpxTY9ELUNWIelrqi6Coq3KvOMhSZSR7ryBgagNhudLyYsAS9f8ldr7ZJyogJDTPg85EJWoAzbqBQl6rFXxVQOssLCFmhMBIqSCGglXKWKVpT5tJezaGHgj850NS3AXavrbNjpNDkKI9sYBigSzDBwcKEhnpQcLAYS8J8ViA0zEpOnPMA51qnQiWzFpIP+KaJBdqanWJ6i2zJRXdBLWncZoqMcdedv+1jwb1zFAGwnS/ywPOBRhKxP/ZflX9vA4TruIOV2GHq7DFGBOuwxaZAjZhrPsVUyd0dtlAwOPVeIPrtMUrwxbbnDDliJwDdgxwiMhJqFywKEWNGwiaS0FZDDocIMuRdw+E4HMszLXZFZqdMQfygAD0AHGbWGk5oDeYkQMBBYQWydhUhhrmXF1i1CLpBolZyBsWVWJdKijEoSCmjJSKqBChIMWCIWasYt4b4GnB0kXEFTDs++Ry38sM6YrTh4Idj+yzgEQM0oaojAH6KpFAiRGq61WCtcLIugBy2C9neVta+pLUoqszm0oumqgBoJSqWrF5nYaoNRZCWxwpmhudmhvSMQb5742Nvh3WJ2bGxtJAIUz3pAs4E2CYOOJ3tm+on9dhwqvDDa7CDl+VhBlky5ApDQgA1BJwgDIN3uJKqzy9mrbYDRIRmQuBmTAFRtlFFLNab4OGLFN1XYassfgEFKL+BjMavTf65sMnb5PbnlXHLKQN7MFhvrt3b4bWyTpPw6AAYcbF2IAhr0V9qExhk5FWGWnISClXQIihYNBBOYQFYJgxBlMXApXGHBbUiN7onPdYw1wmjmKUJrF5lNQ8J+xAL5gRciDkVUAYexei3LvDD23JVSy/J13UxakPzECIrU9YUFsxP7iyE8ugNNWhvkdlc/LsuOWjnNKvZjYGqU+Bl99dOZWAP9he1c+rkLEtCW8YbhQUWvl4RGDAVIvDRr36laoYFt/wVek1bIusXlU4IBf5/U0csIuMEiOYAoq5OggIO+oKXFjgYWGSDu0oG5m1n1A9kg8xfneJVhaQVL0kx/HE11GwlOma+xAlvNa8DtXqvXIuybXYFGgtoDCsZGVxYwiRWNSIkOv/MGMIc2AAGv0XUFAbwQFVIjMJY6gMQfY3FhGooHDAKkwYYhawiowpsLC+PTuDXu9IugAyASX00aA+5HzvgfTfmeu3vnfqYS0zZ+HvRTtJjDVBj2OzJ7BVZ9L4GlMnqm2B5CQ1x+ZIvxK7AlUPhDHekEUVvm9Zu/MABg74g6cNGDZpkqQoVqYA1EVuVyXrDZxafAMKAhWsULALshbFq+EG2zSILYJJVp8kKSobAmNHjIkimKA5/JBBPqGOcO/WtLyKGipdbMXsZofo7AzE0kEWjI57sw2wiCrVIBlEfSGNV9hzP1IDg2pTqIPDinw4QBjMHQbwqgCrAhrE47BaT1gPUuPCGELUWT+FgkSiSniZD34vcQEcTLzqkAgoS6uwUtGVQxmFGBMHJBIGE2NBiIwcWWphRAE7ioRg2aIDo0wyCGU6nt27AzK38zAaqzemyPqBWJ9P0ZD4InfFnq2lxlvYcws1N2bnQR1NlQD6QLK9EFe9FEYFB+psDLfMKEfkLIAhF8Ifv7YBIHqp0X+bqSPZylQZK5pq/ILMPlkXqBGPxTV2uAlPMcaIkVMNlx5IOnUM0qleCwU3NEBrx1ZwoBp8oBZeBlg9E6ZfmteiJmHVDL996ewLfpfSf784c2kzOoeH5mrU3Amd2axT+bRpAwVzRVZQqDEKAgpxVZCGCcOQsRkmPBmkAM4QMxKVTh0wtjCf+ec2Bs8QbKFi2d5+V4wlLIipGG1ikGzbzCTt0ucYYkaJoTEGu341QgpDkgFZ76nd3jvbGKg+R6PpxA4oWCm8RrzWehyEZvR2mZTFMZzOVeltDe3UXX/opFMjVBU2z8Q9g5zOAhi4ELZPNfqIgGmK1U3VOqRUbboqA0IoGDgjO1QfqGCjT+s6bHWhmmaclMhIm/WacWhLwEgNHMiCmMD6sBUYNE+CrWMU1JBnr1LcV6jocTJAFp2pOsohT4XdLwuUMZdXidCCH+hi7s2mwGt1Ra4yYioYVhNWKWM9TNikCVfDDps4ipHRzfaRuAKFBwY/8L0YCAij424b0BsfC1PnZfLbCwcBJwRMJM+vAkPg5p1IDB6pgkRJovqbIXIv3P2Yt3LpflsCHeszYjR1IqO5sQu3BYpMLXR2BQuBNtDq6ivMbQvH+hTvv8xNSZkRxpfc+IhCKE9bU8Y14TV9n0LBKkz4SlzjKuzwWlhrubeMQXXSgaQDy6I1Gdc0IocbYQtJirusw4h1mDCEZYvMWCf0oJXISWpEKgpzhhYWRUX2moBlg/Ng+OGB657pf5UlVoM8t9wJQH3iM4prDNkFxnSg4NSHvCngFQPrjLQWW0JKGeuUsU6NKVylHZ7EcQ8EDAACMeLMd7ZkY7B9Dt3zPBuZBhQeIAQYdDsx1iCsYrMzpFQwTQwkBk+yjiklAhljGCQupEy0N3seA4al76hW4EIFharbB/RUnpTO2/E0dNeWUiwJ1bZQmYMZHkn+1xD9qv4sdCTdxTMGKqw1MfkQkb1VzgYY6KlOkyQLhYwE3ATGkDLWcY1NnHCdVrgpAwbK2NEOZpWIYKyoYEOEAayRLUCMrRTcVdliQ1P1aMwt3lwIE5OqB0EMypqgUwrqAjZWg6GGGgfSxVVVFnTXSj+72gx3FKOR7jy1tJezMXQZkh4U1qI+0CYjrSesN2JHWCVhCes4YRNHXCWtpRl31duTQhG1Tu/ZsGDuXto2/y7ORqaly5t4oKjfUQOMkSMKEVZBPCNDzMoaCkoqnfGRk7kwG2vYi2M45pVYBAbU51jreURlEFkYZjBXkk4WRR+4qQ9WY9N7I7y9wVfMghohq57pm8tAC6JCp05Y1ONLzxioAOmp6swglMIoFLELjKdpwCpmPEkjvjKtsU1PseEoC936ACcCBhAG0ik3jBi4aLDUFq+WAddhp5RUblhHZUuQ+HuIPaFohWnLeeeIWhbOfMw1M1OFA2D1F+bjpAOFmXVbPtg/SeaWD4S60hB8/Qe031Tjo2MMrhpTTd1dlwoKmyc7XK93uBrGCgibqGt1KCisg5TQs8GcOewNbC9LwGDqhf/Ocl8yCAO37RnN3uDPlZUVFhACSyzDELKCg0RBxphQzAjpVz5PwvSsrF+e3b/jqgQwv90SwIQaYSjvWWPeVJ1TBinBReyqNjkDZBeQ1asSok5wf26f1TsX7cMdY1B1IuRy74noJGAgos8B+DKkiNTEzO8iojcB+NcA3gbgcwD+DjP/oe7/AQDv1f3/LjP/+6MnKEB8raFiBsAhoKSIbUp4mhKeDgOe5gGv5RXWNGHnDIviFgPWlBBJOlzkjDVnbDhjxyO2FHEdtu6UVA1bBYQpRxSNdciT5BrwFFCyWLg5uwfHUq6N/IBknGzoqTNXl+3X3tQqhRoKXT0eC8JHQUFTpwcGhoK4ylhvRlyvd3jj5imu0q4CwjpMFRCugkSceruB6fmH5Bhj8DYGL54xFA7VZlQ05L2+1+c8IqIEwipMSMoYhliQUkaedF2GSQKaygSQlvKjQZYQ2GOJtwCDv+eN9TXmAAUJKw8YrFAMZNkDCqizdl+ezasTLn6hK6Mnlm/SpfYONtP1u+aqhCzEM5bDauwtchfG8NeY+ffc5/cD+Dgz/ygRvV8//wMieieAdwP4RgB/GsB/JKI/x8wHew4p+zfhQAhJAlNKjhhzxDYn7EoUOglSY5VarMGIAAaKFbmvAFkYlxg7LthwwZozxvgnuOFBSsLFNXYp4SYPeJoGbFPEVDuYe1D+VW0KOFwA5DlLjXrsoh8bpebIzR2ZJlwNI14ZJABsHSeswtQBwjqMmsTWetWc9s/lGDCs6ITYXMpOfciuKhfXGAYAGEkCnFIoUoMjCOiQDqQ+poFbmLEyvu6+HfNKzECDQdW9LBm1aPElDccBRl8RLDTvUSv+Cu1Xs4hHM2QfA6xDt8/ZPSRfguuKXPeRezShyvcA+JC+/xCA73XbP8zMW2b+LQCfBfDNDzjPo8g8LD5iP27/HAb5o8vsopbGgqWst8/3100v8vqQU4GBAfwHIvpFInqfbvt6Zv4iAOj/r9Pt3wDgf7nffl63dUJE7yOiTxDRJ6bXvnK/1l/kIhd5JnKqKvEtzPwFIvo6AB8jol8/su/SxLs3BTHzBwF8EACevPmtlynqIhc5IzmJMTDzF/T/lwD8FEQ1+B0iejMA6P8v6e6fB/BW9/O3APjCYzX4Ihe5yLOXW4GBiK6J6FV7D+BvAfgkgI8CeI/u9h4AP63vPwrg3US0JqK3A3gHgJ9/7IZf5CIXeXZyiirx9QB+SpduTwB+kpn/HRH9AoCPENF7Afw2gO8DAGb+FBF9BMCvQQJDf+CYR+IiF7nI+QnxPdMyH7URRL8L4CsAfu+2fc9AvgaXdj62vCxtfVnaCSy39c8w89ee8uOzAAYAIKJPMPO7XnQ7bpNLOx9fXpa2viztBB7e1ofEMVzkIhd5ncoFGC5ykYvsyTkBwwdfdANOlEs7H19elra+LO0EHtjWs7ExXOQiFzkfOSfGcJGLXORM5IUDAxF9OxF9hog+q1maL7o9/4KIvkREn3Tb3kREHyOi39D/b3TffUDb/hki+rbn2M63EtF/IqJPE9GniOgHz7GtRLQhop8nol/Rdv6jc2ynO3ckol8iop8583Z+joj+OxH9MhF94tHbyrpyzot4QVaK+E0AfxbACsCvAHjnC27TXwXwTQA+6bb9EwDv1/fvB/CP9f07tc1rAG/Xa4nPqZ1vBvBN+v5VAP9D23NWbYXkzryi7wcA/xXAXz63drr2/n0APwngZ8712ev5Pwfga2bbHq2tL5oxfDOAzzLz/2TmHYAPQ9K2X5gw838G8AezzWeXYs7MX2Tm/6bvvwzg05As1rNqK4v8iX4c9MXn1k4AIKK3APhOAD/uNp9dO4/Io7X1RQPDSSnaZyAPSjF/1kJEbwPwlyCz8dm1Ven5L0MS7T7GzGfZTgA/BuCH0BdEO8d2As+gFIKXF13z8aQU7TOWF95+InoFwL8B8PeY+Y/pcA37F9ZWllyZv0hEXw3Ju/nzR3Z/Ie0kou8C8CVm/kUi+tZTfrKw7Xk++0cvheDlRTOGlyVF+yxTzIlogIDCv2Tmf3vObQUAZv6/AH4OwLfj/Nr5LQC+W+ubfhjAXyeinzjDdgJ49qUQXjQw/AKAdxDR24loBakV+dEX3KYlObsUcxJq8M8BfJqZ/+m5tpWIvlaZAojoCYC/CeDXz62dzPwBZn4LM78N0g9/lpm//9zaCTynUgjPy4p6xLr6HRCL+m8C+OEzaM+/AvBFACMEad8L4E8B+DiA39D/b3L7/7C2/TMA/vZzbOdfgdDBXwXwy/r6jnNrK4C/AOCXtJ2fBPAPdftZtXPW5m9F80qcXTshXrxf0denbNw8ZlsvkY8XuchF9uRFqxIXuchFzlAuwHCRi1xkTy7AcJGLXGRPLsBwkYtcZE8uwHCRi1xkTy7AcJGLXGRPLsBwkYtcZE8uwHCRi1xkT/4fDDOil/ZXMbMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = BlackPointerSegDataset(folder,None)\n",
    "print(len(dataset))\n",
    "img, mask = dataset[76]\n",
    "print(img.shape, mask.shape)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "# plt.imshow(img.numpy())\n",
    "# plt.figure()\n",
    "plt.imshow(img[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float32')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 定义分割网络结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Program Files\\Python39\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "c:\\Program Files\\Python39\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=DeepLabV3_ResNet50_Weights.COCO_WITH_VOC_LABELS_V1`. You can also use `weights=DeepLabV3_ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n",
      "Downloading: \"https://download.pytorch.org/models/deeplabv3_resnet50_coco-cd0a2569.pth\" to D:\\Caches\\TORCH_HOME\\hub\\checkpoints\\deeplabv3_resnet50_coco-cd0a2569.pth\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "781e93d784034a6684e43a61cc343b46",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/161M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n",
    "Net = torchvision.models.segmentation.deeplabv3_resnet50(pretrained=True)\n",
    "Net.classifier[4] = torch.nn.Conv2d(256, 2, kernel_size=(1, 1), stride=(1, 1)) # Change final layer to 3 classes\n",
    "Net=Net.to(device)\n",
    "optimizer=torch.optim.Adam(params=Net.parameters(),lr=Learning_Rate) # Create adam optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving Model0.torch\n",
      "Saving Model1000.torch\n",
      "Saving Model2000.torch\n",
      "Saving Model3000.torch\n",
      "Saving Model4000.torch\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_24452/12802351.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     11\u001b[0m    \u001b[0mLoss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcriterion\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPred\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mann\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlong\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Calculate cross entropy loss\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m    \u001b[0mLoss\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Backpropogate loss\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m    \u001b[0moptimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Apply gradient descent change to weight\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m    \u001b[1;32mif\u001b[0m \u001b[0mitr\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;36m1000\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Program Files\\Python39\\lib\\site-packages\\torch\\optim\\optimizer.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    138\u001b[0m                 \u001b[0mprofile_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Optimizer.step#{}.step\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    139\u001b[0m                 \u001b[1;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecord_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprofile_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 140\u001b[1;33m                     \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    141\u001b[0m                     \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_optimizer_step_code\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    142\u001b[0m                     \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Program Files\\Python39\\lib\\site-packages\\torch\\optim\\optimizer.py\u001b[0m in \u001b[0;36m_use_grad\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m     21\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m             \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_grad_enabled\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdefaults\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'differentiable'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m             \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_grad_enabled\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprev_grad\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Program Files\\Python39\\lib\\site-packages\\torch\\optim\\adam.py\u001b[0m in \u001b[0;36mstep\u001b[1;34m(self, closure, grad_scaler)\u001b[0m\n\u001b[0;32m    232\u001b[0m                     \u001b[0mstate_steps\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'step'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    233\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 234\u001b[1;33m             adam(params_with_grad,\n\u001b[0m\u001b[0;32m    235\u001b[0m                  \u001b[0mgrads\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    236\u001b[0m                  \u001b[0mexp_avgs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Program Files\\Python39\\lib\\site-packages\\torch\\optim\\adam.py\u001b[0m in \u001b[0;36madam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, foreach, capturable, differentiable, fused, grad_scale, found_inf, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize)\u001b[0m\n\u001b[0;32m    298\u001b[0m         \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_single_tensor_adam\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    299\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 300\u001b[1;33m     func(params,\n\u001b[0m\u001b[0;32m    301\u001b[0m          \u001b[0mgrads\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    302\u001b[0m          \u001b[0mexp_avgs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Program Files\\Python39\\lib\\site-packages\\torch\\optim\\adam.py\u001b[0m in \u001b[0;36m_single_tensor_adam\u001b[1;34m(params, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, grad_scale, found_inf, amsgrad, beta1, beta2, lr, weight_decay, eps, maximize, capturable, differentiable)\u001b[0m\n\u001b[0;32m    362\u001b[0m         \u001b[1;31m# Decay the first and second moment running average coefficient\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    363\u001b[0m         \u001b[0mexp_avg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbeta1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mbeta1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 364\u001b[1;33m         \u001b[0mexp_avg_sq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmul_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbeta2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maddcmul_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgrad\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconj\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mbeta2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    365\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    366\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcapturable\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mdifferentiable\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for itr in range(20000): # Training loop\n",
    "   images,ann=next(iter(data_loader))\n",
    "   \n",
    "   images=torch.autograd.Variable(images,requires_grad=False).to(device)    \n",
    "   \n",
    "   ann = torch.autograd.Variable(ann,requires_grad=False).to(device)              \n",
    "   \n",
    "   Pred=Net(images)['out'] # make prediction\n",
    "\n",
    "   criterion = torch.nn.CrossEntropyLoss() # Set loss function\n",
    "   Loss=criterion(Pred,torch.squeeze(ann).long()) # Calculate cross entropy loss\n",
    "   Loss.backward() # Backpropogate loss\n",
    "   optimizer.step() # Apply gradient descent change to weight\n",
    "\n",
    "   if itr % 1000 == 0: \n",
    "      print('Saving Model' +str(itr) + '.torch')\n",
    "      torch.save(Net.state_dict(), str(itr) + '.torch')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 测试效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "Net.eval()\n",
    "\n",
    "images,ann=next(iter(data_loader))\n",
    "images=torch.autograd.Variable(images,requires_grad=False).to(device)    \n",
    "ann = torch.autograd.Variable(ann,requires_grad=False).to(device)              \n",
    "   \n",
    "with torch.no_grad():\n",
    "    Pred=Net(images)['out'] # make prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.imshow(images[1][0].cpu())\n",
    "# plt.figure()\n",
    "# plt.imshow(ann[1][0].cpu())\n",
    "# mask=ann[1][0].cpu().numpy()\n",
    "# print(np.sum(mask))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images,ann=next(iter(data_loader))\n",
    "torch.max(ann)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# %% test the model\n",
    "if mode == 'test':\n",
    "    Net.load_state_dict(torch.load(fn))\n",
    "    Net.eval()\n",
    "\n",
    "\n",
    "plt.rcParams[\"savefig.bbox\"] = 'tight'\n",
    "\n",
    "def show(imgs):\n",
    "    if not isinstance(imgs, list):\n",
    "        imgs = [imgs]\n",
    "    fig, axs = plt.subplots(ncols=len(imgs), squeeze=False)\n",
    "    for i, img in enumerate(imgs):\n",
    "        img = img.detach()\n",
    "        img = tf.ToPILImage(img)\n",
    "        axs[0, i].imshow(np.asarray(img))\n",
    "        axs[0, i].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])\n",
    "\n",
    "layout = [  [sg.Text('共有'+str(len(dataset))+\"个带标注的水表图像\")],\n",
    "            [sg.Text('请输入要查看的图像序号(从0开始):'), sg.InputText('0')],            \n",
    "            [sg.Button('Ok'), sg.Button('Cancel')] ]\n",
    "\n",
    "# Create the Window\n",
    "window = sg.Window('检测结果查看程序', layout)\n",
    "plt.ion()\n",
    "fig = plt.figure()\n",
    "# Event Loop to process \"events\" and get the \"values\" of the inputs\n",
    "while True:\n",
    "    event, values = window.read()\n",
    "    if event == sg.WIN_CLOSED or event == 'Cancel': # if user closes window or clicks cancel\n",
    "        break\n",
    "    if(int(values[0])>=0 and int(values[0])<len(dataset)):\n",
    "        fid = int(values[0])\n",
    "        img,label = dataset.__getitem__(fid)\n",
    "        \n",
    "        x = img.unsqueeze(0)\n",
    "        x = x.to(device)\n",
    "\n",
    "        with torch.no_grad():\n",
    "            predictions = Net(x)['out']        \n",
    "        \n",
    "        plt.cla()\n",
    "        #show the original image\n",
    "        mask = predictions[0]['masks']\n",
    "        # pil_img = tT.ToPILImage()(img)\n",
    "        pil_img = tf.ToPILImage()(mask[0])\n",
    "        plt.imshow(pil_img)\n",
    "\n",
    "               \n",
    "        # draw_im_with_anno(folder, flst[fid], anno)\n",
    "        plt.draw()\n",
    "\n",
    "window.close()\n",
    "\n",
    "# %%\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "11938c6bc6919ae2720b4d5011047913343b08a43b18698fd82dedb0d4417594"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
